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Advanced Quantitative Research Methodology,Lecture Notes:
Matching Methods for Causal Inference1
Gary King2
Institute for Quantitative Social ScienceHarvard University
1 c©Copyright 2015 Gary King, All Rights Reserved.2GaryKing.org.
1 / 44
Matching Overview
• Current practice:
“Matching As Nonparametric Preprocessing For Re-ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory.
So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice:
“Matching As Nonparametric Preprocessing For Re-ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory.
So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory.
So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory.
So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory. So let’s changethe theory:
“A Theory of Statistical Inference for Matching Meth-ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory. So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory. So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical:
“Why Propensity Scores Should Not Be Used forMatching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory. So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical: “Why Propensity Scores Should Not Be Used for
Matching” (Gary King, Richard Nielsen)
• Matching methods optimize either imbalance (≈ bias) or # unitspruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory. So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical: “Why Propensity Scores Should Not Be Used for
Matching” (Gary King, Richard Nielsen)• Matching methods optimize either imbalance (≈ bias) or # units
pruned (≈ variance); users need both simultaneously’:
“The Balance-Sample Size Frontier in MatchingMethods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Matching Overview• Current practice: “Matching As Nonparametric Preprocessing For Re-
ducing Model Dependence In Parametric Causal Infer-ence” (Daniel Ho, Kosuke Imai, Gary King, ElizabethStuart)
• Current practice violates current statistical theory. So let’s changethe theory: “A Theory of Statistical Inference for Matching Meth-
ods in Applied Causal Research”(Stefano Iacus, Gary King, Giuseppe Porro)
• The most popular method (propensity score matching, used in49,600 articles!) sounds magical: “Why Propensity Scores Should Not Be Used for
Matching” (Gary King, Richard Nielsen)• Matching methods optimize either imbalance (≈ bias) or # units
pruned (≈ variance); users need both simultaneously’: “The Balance-Sample Size Frontier in Matching
Methods for Causal Inference” (Gary King, Christo-pher Lucas and Richard Nielsen)
2 / 44
Overview of Matching for Causal Inference
• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence
• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach
• Makes parametric models work better rather than substitutefor them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data
• Violates the “more data is better” principle, but that onlyapplies when you know the DGP
• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP
• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Overview of Matching for Causal Inference• Goal: reduce model dependence• A nonparametric, non-model-based approach• Makes parametric models work better rather than substitute
for them (i.e,. matching is not an estimator; its apreprocessing method)
• Should have been called pruning (no bias is introduced ifpruning is a function of T and X , but not Y )
• Apply model to preprocessed (pruned) rather than raw data• Violates the “more data is better” principle, but that only
applies when you know the DGP• Overall idea:
• If each treated unit exactly matches a control unit w.r.t. X ,then: (1) treated and control groups are identical, (2) X is nolonger a confounder, (3) no need to worry about the functionalform (ȲT − ȲC is good enough).
• If treated and control groups are better balanced than whenyou started, due to pruning, model dependence is reduced
3 / 44
Model Dependence: A Simpler Example
What to do?
• Preprocess I: Eliminate extrapolation region• Preprocess II: Match (prune) within interpolation region• Model remaining imbalance (as you would w/o matching)
4 / 44
Model Dependence: A Simpler Example(King and Zeng, 2006: fig.4 Political Analysis)
What to do?
• Preprocess I: Eliminate extrapolation region• Preprocess II: Match (prune) within interpolation region• Model remaining imbalance (as you would w/o matching)
4 / 44
Model Dependence: A Simpler Example(King and Zeng, 2006: fig.4 Political Analysis)
What to do?
• Preprocess I: Eliminate extrapolation region• Preprocess II: Match (prune) within interpolation region• Model remaining imbalance (as you would w/o matching)
4 / 44
Model Dependence: A Simpler Example(King and Zeng, 2006: fig.4 Political Analysis)
What to do?
• Preprocess I: Eliminate extrapolation region• Preprocess II: Match (prune) within interpolation region• Model remaining imbalance (as you would w/o matching)
4 / 44
Model Dependence: A Simpler Example(King and Zeng, 2006: fig.4 Political Analysis)
What to do?
• Preprocess I: Eliminate extrapolation region
• Preprocess II: Match (prune) within interpolation region• Model remaining imbalance (as you would w/o matching)
4 / 44
Model Dependence: A Simpler Example(King and Zeng, 2006: fig.4 Political Analysis)
What to do?
• Preprocess I: Eliminate extrapolation region• Preprocess II: Match (prune) within interpolation region
• Model remaining imbalance (as you would w/o matching)
4 / 44
Model Dependence: A Simpler Example(King and Zeng, 2006: fig.4 Political Analysis)
What to do?
• Preprocess I: Eliminate extrapolation region• Preprocess II: Match (prune) within interpolation region• Model remaining imbalance (as you would w/o matching)
4 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.
• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points
2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.
4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Remove Extrapolation Region, then Match
• Must remove data (selecting on X ) to avoid extrapolation.• Options to find “common support” of p(X |T = 1) andP(X |T = 0)
1. Exact match, so support is defined only at data points2. Less but still conservative: convex hull approach
• let T ∗ and X ∗ denote subsets of T and X s.t. {1− T ∗,X ∗}falls within the convex hull of {T ,X}
• use X ∗ as estimate of common support (deleting remainingobservations)
3. Other approaches, based on distance metrics, pscores, etc.4. Easiest: Coarsened Exact Matching, no separate step needed
5 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
T
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
T
CC
C
CC
C
C
C
C
C
C
C
C
C
C
C
C
CC C
C
C
C
C
C
C
C
C
C
C
C
CCC
CC
CC
C
C
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
T
CC
C
CC
C
C
C
C
C
C
C
C
C
C
C
C
CC C
C
C
C
C
C
C
C
C
C
C
C
CCC
CC
CC
C
C
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
T
CC
C
CC
C
C
C
C
C
C
C
C
C
C
C
C
CC C
C
C
C
C
C
C
C
C
C
C
C
CCC
CC
CC
C
C
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
T
CC
C
CC
C
C
C
C
C
C
C
C
C
C
C
C
CC C
C
C
C
C
C
C
C
C
C
C
C
CCC
CC
CC
C
C
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
TC
C
C
C
C
CC
C
C
CC
C CC
C
C
CCCC
C
CC
C
CC
CC
CC
C
C
C
C
CC
CCCC
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Education (years)
Out
com
e
12 14 16 18 20 22 24 26 28
0
2
4
6
8
10
12
T
T
T
T T
T
T
TTT
TT
T TT T
T
T
T
TC
C
C
C
C
CC
C
C
CC
C CC
C
C
CCCC
C
CC
C
CC
CC
CC
C
C
C
C
CC
CCCC
6 / 44
Matching within the Interpolation Region(Ho, Imai, King, Stuart, 2007: fig.1, Political Analysis)
Matching reduces model dependence, bias, and variance
6 / 44
Empirical Illustration: Carpenter, AJPS, 2002
• Hypothesis: Democratic senate majorities slow FDA drugapproval time
• n = 408 new drugs (262 approved, 146 pending).• lognormal survival model.• seven oversight variables (median adjusted ADA scores for
House and Senate Committees as well as for House andSenate floors, Democratic Majority in House and Senate, andDemocratic Presidency).
• 18 control variables (clinical factors, firm characteristics,media variables, etc.)
7 / 44
Empirical Illustration: Carpenter, AJPS, 2002
• Hypothesis: Democratic senate majorities slow FDA drugapproval time
• n = 408 new drugs (262 approved, 146 pending).• lognormal survival model.• seven oversight variables (median adjusted ADA scores for
House and Senate Committees as well as for House andSenate floors, Democratic Majority in House and Senate, andDemocratic Presidency).
• 18 control variables (clinical factors, firm characteristics,media variables, etc.)
7 / 44
Empirical Illustration: Carpenter, AJPS, 2002
• Hypothesis: Democratic senate majorities slow FDA drugapproval time
• n = 408 new drugs (262 approved, 146 pending).
• lognormal survival model.• seven oversight variables (median adjusted ADA scores for
House and Senate Committees as well as for House andSenate floors, Democratic Majority in House and Senate, andDemocratic Presidency).
• 18 control variables (clinical factors, firm characteristics,media variables, etc.)
7 / 44
Empirical Illustration: Carpenter, AJPS, 2002
• Hypothesis: Democratic senate majorities slow FDA drugapproval time
• n = 408 new drugs (262 approved, 146 pending).• lognormal survival model.
• seven oversight variables (median adjusted ADA scores forHouse and Senate Committees as well as for House andSenate floors, Democratic Majority in House and Senate, andDemocratic Presidency).
• 18 control variables (clinical factors, firm characteristics,media variables, etc.)
7 / 44
Empirical Illustration: Carpenter, AJPS, 2002
• Hypothesis: Democratic senate majorities slow FDA drugapproval time
• n = 408 new drugs (262 approved, 146 pending).• lognormal survival model.• seven oversight variables (median adjusted ADA scores for
House and Senate Committees as well as for House andSenate floors, Democratic Majority in House and Senate, andDemocratic Presidency).
• 18 control variables (clinical factors, firm characteristics,media variables, etc.)
7 / 44
Empirical Illustration: Carpenter, AJPS, 2002
• Hypothesis: Democratic senate majorities slow FDA drugapproval time
• n = 408 new drugs (262 approved, 146 pending).• lognormal survival model.• seven oversight variables (median adjusted ADA scores for
House and Senate Committees as well as for House andSenate floors, Democratic Majority in House and Senate, andDemocratic Presidency).
• 18 control variables (clinical factors, firm characteristics,media variables, etc.)
7 / 44
Evaluating Reduction in Model Dependence
• Focus on the causal effect of a Democratic majority in theSenate (identified by Carpenter as not robust).
• Match: prune 49 units (2 treated, 17 control units).• run 262,143 possible specifications and calculates ATE for
each.
• Look at variability in ATE estimate across specifications.• (Normal applications would only use one or a few
specifications.)
8 / 44
Evaluating Reduction in Model Dependence
• Focus on the causal effect of a Democratic majority in theSenate (identified by Carpenter as not robust).
• Match: prune 49 units (2 treated, 17 control units).• run 262,143 possible specifications and calculates ATE for
each.
• Look at variability in ATE estimate across specifications.• (Normal applications would only use one or a few
specifications.)
8 / 44
Evaluating Reduction in Model Dependence
• Focus on the causal effect of a Democratic majority in theSenate (identified by Carpenter as not robust).
• Match: prune 49 units (2 treated, 17 control units).
• run 262,143 possible specifications and calculates ATE foreach.
• Look at variability in ATE estimate across specifications.• (Normal applications would only use one or a few
specifications.)
8 / 44
Evaluating Reduction in Model Dependence
• Focus on the causal effect of a Democratic majority in theSenate (identified by Carpenter as not robust).
• Match: prune 49 units (2 treated, 17 control units).• run 262,143 possible specifications and calculates ATE for
each.
• Look at variability in ATE estimate across specifications.• (Normal applications would only use one or a few
specifications.)
8 / 44
Evaluating Reduction in Model Dependence
• Focus on the causal effect of a Democratic majority in theSenate (identified by Carpenter as not robust).
• Match: prune 49 units (2 treated, 17 control units).• run 262,143 possible specifications and calculates ATE for
each.
• Look at variability in ATE estimate across specifications.
• (Normal applications would only use one or a fewspecifications.)
8 / 44
Evaluating Reduction in Model Dependence
• Focus on the causal effect of a Democratic majority in theSenate (identified by Carpenter as not robust).
• Match: prune 49 units (2 treated, 17 control units).• run 262,143 possible specifications and calculates ATE for
each.
• Look at variability in ATE estimate across specifications.• (Normal applications would only use one or a few
specifications.)
8 / 44
Reducing Model Dependence
−80 −70 −60 −50 −40 −30
0.00
0.05
0.10
0.15
0.20
Estimated in−sample average treatment effect for the treated
Den
sity
Raw data Matcheddata
Point estimate of Carpenter's specification
using raw data
Figure: SATT Histogram: Effect of Democratic Senate majority on FDAdrug approval time, across 262, 143 specifications.
9 / 44
Another Example: Jeffrey Koch, AJPS, 2002
−0.05 0.00 0.05 0.10
010
2030
4050
60
Estimated average treatment effect
Den
sity
Raw data
Matcheddata
Point estimate of raw data
Figure: SATT Histogram: Effect of being a highly visible femaleRepublican candidate across 63 possible specifications with the Kochdata.
10 / 44
The Advantage of Matching
Without Matching:
11 / 44
The Advantage of Matching
Without Matching:
Imbalance
11 / 44
The Advantage of Matching
Without Matching:
Imbalance Model Dependence
11 / 44
The Advantage of Matching
Without Matching:
Imbalance Model Dependence Researcher discretion
11 / 44
The Advantage of Matching
Without Matching:
Imbalance Model Dependence Researcher discretion Bias
11 / 44
The Advantage of Matching
With��HHout Matching:
��ZZImbalance Model Dependence Researcher discretion Bias
11 / 44
The Advantage of Matching
With��HHout Matching:
��ZZImbalance (((((((
((hhhhhhhhhModel Dependence Researcher discretion Bias
11 / 44
The Advantage of Matching
With��HHout Matching:
��ZZImbalance (((((((
((hhhhhhhhhModel Dependence ((((((((
((hhhhhhhhhhResearcher discretion Bias
11 / 44
The Advantage of Matching
With��HHout Matching:
��ZZImbalance (((((((
((hhhhhhhhhModel Dependence ((((((((
((hhhhhhhhhhResearcher discretion ���XXXBias
11 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders
• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi (1)− Yi (0)
= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi (1)− Yi (0)= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:
1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control
• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence
• Big convenience: Follow preprocessing with whateverstatistical method you’d have used without matching
12 / 44
Current Practice: Matching as Preprocessing
• Yi dep var, Ti (1=treated, 0=control), Xi confounders• Treatment Effect for treated observation i :
TEi = Yi − Yi (0)= observed− unobserved
• Quantities of Interest:1. SATT: Sample Average Treatment effect on the Treated:
SATT = meani∈{Ti=1} (TEi )
2. FSATT: Feasible Average Treatment effect on the Treated
• Estimate Yi (0) with Yj from matched (Xi ≈ Xj) control• Prune nonmatches: reduces imbalance & model dependence• Big convenience: Follow preprocessing with whatever
statistical method you’d have used without matching
12 / 44
Approximating Randomized Experiments
• Types of experiments:
1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:
1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average
Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample
Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization
for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:
imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance,
model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence,
power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power,
efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency,
bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,
research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs,
and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?
• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?• PSM: complete randomization
• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM
(but wait, it gets worse for PSM)
13 / 44
Approximating Randomized Experiments
• Types of experiments:1. Compete Randomization: Treatment assignment by coin flips
Balance on X : only on average Balance on unmeasured vars: only on average
2. Fully Blocked: Match pairs on X (exactly), then flip coins
Balance on X : perfect in sample Balance on unmeasured vars: only on average
• Fully blocked dominates complete randomization for:imbalance, model dependence, power, efficiency, bias,research costs, and robustness.
• Matching methods approximate which experiment?• PSM: complete randomization• Other methods: fully blocked
• =⇒ As we show, other methods usually dominate PSM(but wait, it gets worse for PSM)
13 / 44
Method 1: Mahalanobis Distance Matching
1. Preprocess (Matching)
• Distance(Xi ,Xj) =√
(Xi − Xj)′S−1(Xi − Xj)• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)
• Distance(Xi ,Xj) =√
(Xi − Xj)′S−1(Xi − Xj)• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)
• Distance(Xi ,Xj) =√
(Xi − Xj)′S−1(Xi − Xj)• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Distance(Xi ,Xj) =
√(Xi − Xj)′S−1(Xi − Xj)
• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Distance(Xi ,Xj) =
√(Xi − Xj)′S−1(Xi − Xj)
• Match each treated unit to the nearest control unit
• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Distance(Xi ,Xj) =
√(Xi − Xj)′S−1(Xi − Xj)
• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused
• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Distance(Xi ,Xj) =
√(Xi − Xj)′S−1(Xi − Xj)
• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Method 1: Mahalanobis Distance Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Distance(Xi ,Xj) =
√(Xi − Xj)′S−1(Xi − Xj)
• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model
3. Checking Measure imbalance, tweak, repeat, . . .
14 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
15 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
TTTT
T
T
T
T
T
T
T
T
T
TT
T
T
T
T
T
15 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
C
C
CC
C
C
C
C
C
CC
C
CCC
CC
C
C
C
CC CC
C
C
CC
C
CC
CC
C
C C
CC
C
C
TTTT
T
T
T
T
T
T
T
T
T
TT
T
T
T
T
T
15 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
C
C
CC
C
C
C
C
C
CC
C
CCC
CC
C
C
C
CC CC
C
C
CC
C
CC
CC
C
C C
CC
C
C
TTTT
T
T
T
T
T
T
T
T
T
TT
T
T
T
T
T
15 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
T TT T
TTTT
T TTTT
T TT
TTTT
CCC C
CC
C
C
C CC
C
CC
CCC CC
C
C
CCCCC
CCC CCCCC
C CCCC
C
15 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
T TT T
TTTT
T TTTT
T TT
TTTT
CCC C
CC
C
C
C CC
C
CC
CCC CC
C
15 / 44
Mahalanobis Distance Matching
Education (years)
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
T TT T
TTTT
T TTTT
T TT
TTTT
CCC C
CC
C
C
C CC
C
CC
CCC CC
C
15 / 44
Method 2: Coarsened Exact Matching
1. Preprocess (Matching)
• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)
• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)
• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)
• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)
• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)
• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)
• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )
• Prune any stratum with 0 treated or 0 control units• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model
• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .
• Easier, but still iterative
16 / 44
Method 2: Coarsened Exact Matching(Approximates Fully Blocked Experiment)
1. Preprocess (Matching)• Temporarily coarsen X as much as you’re willing
• e.g., Education (grade school, high school, college, graduate)• Apply exact matching to the coarsened X , C (X )
• Sort observations into strata, each with unique values of C(X )• Prune any stratum with 0 treated or 0 control units
• Pass on original (uncoarsened) units except those pruned
2. Estimation Difference in means or a model• Need to weight controls in each stratum to equal treateds
3. Checking Determine matched sample size, tweak, repeat, . . .• Easier, but still iterative
16 / 44
Coarsened Exact Matching
17 / 44
Coarsened Exact Matching
Education
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
CCC C
CC CC
C CC C CCC CCC
CCCC CC CC
C CCCCCC
C CCC CC
C
T TT T
TTTT
T TTTT
T TT
TTTT
17 / 44
Coarsened Exact Matching
Education
HS BA MA PhD 2nd PhD
Drinking age
Don't trust anyoneover 30
The Big 40
Senior Discounts
Retirement
Old
CCC C
CC CC
C CC C CCC CCC
CCCC CC CC
C CCCCCC
C CCC CC
C
T TT T
TTTT
T TTTT
T TT
TTTT
17 / 44
Coarsened Exact Matching
Education
HS BA MA PhD 2nd PhD
Drinking age
Don't trust anyoneover 30
The Big 40
Senior Discounts
Retirement
Old
CCC C
CC CC
C CC C CCC CCC
CCCC CC CC
C CCCCCC
C CCC CC
C
T TT T
TTTT
T TTTT
T TT
TTTT
17 / 44
Coarsened Exact Matching
Education
HS BA MA PhD 2nd PhD
Drinking age
Don't trust anyoneover 30
The Big 40
Senior Discounts
Retirement
Old
CC C
CC
CC CCCC CC C
CCCC
TTT T T
TTT
T TT
TTTT
17 / 44
Coarsened Exact Matching
Education
HS BA MA PhD 2nd PhD
Drinking age
Don't trust anyoneover 30
The Big 40
Senior Discounts
Retirement
Old
CC C
CCCC C CC
C CC CCC
C
C
TTT T T
TTT
T TT
TTTT
17 / 44
Coarsened Exact Matching
Education
Age
12 14 16 18 20 22 24 26 28
20
30
40
50
60
70
80
CC C
CCCC C CC
C CC CCC
C
C
TTT T T
TTT
T TT
TTTT
17 / 44
Method 3: Propensity Score Matching
1. Preprocess (Matching)
• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)
• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)
• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |
• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit
• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused
• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Method 3: Propensity Score Matching(Approximates Completely Randomized Experiment)
1. Preprocess (Matching)• Reduce k elements of X to scalarπi ≡ Pr(Ti = 1|X ) = 11+e−Xiβ
• Distance(Xi ,Xj) = |πi − πj |• Match each treated unit to the nearest control unit• Control units: not reused; pruned if unused• Prune matches if Distance>caliper
2. Estimation Difference in means or a model3. Checking Measure imbalance, tweak, repeat, . . .
18 / 44
Propensity Score Matching
Education (years)
Age
12 16 20 24 28
20
30
40
50
60
70
80
C
C
CC
C
C
C
C
C
CC
C
CCC
CC
C
C
C
CCCC
C
C
CC
C
CC
CC
C
C C
CC
C
C
TTTT
T
T
T
T
T
T
T
T
T
TT
T
T
T
T
T
19 / 44
Propensity Score Matching
Education (years)
Age
12 16 20 24 28
20
30
40
50
60
70
80
C
C
CC
C
C
C
C
C
CC
C
CCC
CC
C
C
C
CCCC
C
C
CC
C
CC
CC
C
C C
CC
C
C
TTTT
T
T
T
T
T
T
T
T
T
TT
T
T
T
T
T
1
0
PropensityScore 19 / 44
Propensity Score Matching
Education (years)
Age
12 16 20 24 28
20
30
40
50
60
70
80
C
C
CC
C
C
C
C
C
CC
C
CCC
CC
C
C
C
CCCC
C
C
CC
C
CC
CC
C
C C
CC
C
C
TTTT
T
T
T
T
T
T
T
T
T
TT
T
T
T
T
T
1
0
PropensityScore
C
C
CC
CCC
C
C
C
CC
C
C
C
C
C
C
C
CCCCC
C
CCCCCCCCC
C
C
C
C
CC
T
TTT
T
TT
T
T
T
T
TT
T
T
T
T
T
T
T
19