Micro-Econometrics for Policy, Program, and

This title includes the following features: First book on treatmenteffects in econometrics that is suitable as a textbook; Provides a survey of theburgeoning econometric literature on treatment effects

Myoung-jae Lee is Associate Professor in the School of Economics and Social Sciences at Singapore Management University. He has held positions at Pennsylvania State University, Tilburg University in The Netherlands, London School of Economics, Tsukuba University in Japan and Sungkyunkwan University in Seoul.

1 Tour of the book

1

(6)

2 Basics of treatment effect analysis

7

(36)

2.1 Treatment intervention, counter-factual, and causal relation

7

(4)

2.1.1 Potential outcomes and intervention

7

(2)

2.1.2 Causality and association

9

(1)

2.1.3 Partial equilibrium analysis and remarks

10

(1)

2.2 Various treatment effects and no effects

11

(5)

2.2.1 Various effects

11

(2)

2.2.2 Three no-effect concepts

13

(1)

2.2.3 Further remarks

14

(2)

2.3 Group-mean difference and randomization

16

(5)

2.3.1 Group-mean difference and mean effect

16

(2)

2.3.2 Consequences of randomization

18

(1)

2.3.3 Checking out covariate balance

19

(2)

2.4 Overt bias, hidden (covert) bias, and selection problems

21

(5)

2.4.1 Overt and hidden biases

21

(1)

2.4.2 Selection on observables and unobservables

22

(3)

2.4.3 Linear models and biases

25

(1)

2.5 Estimation with group mean difference and LSE

26

(6)

2.5.1 Group-mean difference and LSE

26

(2)

2.5.2 A job-training example

28

(2)

2.5.3 Linking counter-factuals to linear models

30

(2)

2.6 Structural form equations and treatment effect

32

(3)

2.7 On mean independence and independence

35

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2.7.1 Independence and conditional independence

35

(1)

2.7.2 Symmetric and asymmetric mean-independence

36

(1)

2.7.3 Joint and marginal independence

37

(1)

2.8 Illustration of biases and Simpson's Paradox

38

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2.8.1 Illustration of biases

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(2)

2.8.2 Source of overt bias

40

(1)

2.8.3 Simpson's Paradox

41

(2)

3 Controlling for covariates

43

(36)

3.1 Variables to control for

43

(6)

3.1.1 Must cases

44

(1)

3.1.2 No-no cases

45

(1)

3.1.3 Yes/no cases

46

(1)

3.1.4 Option case

47

(1)

3.1.5 Proxy cases

48

(1)

3.2 Comparison group and controlling for observed variables

49

(7)

3.2.1 Comparison group bias

49

(2)

3.2.2 Dimension and support problems in conditioning

51

(2)

3.2.3 Parametric models to avoid dimension and support problems

53

(1)

3.2.4 Two-stage method for a semi-linear model

54

(2)

3.3 Regression discontinuity design (RDD) and before-after (BA)

56

(9)

3.3.1 Parametric regression discontinuity

56

(2)

3.3.2 Sharp nonparametric regression discontinuity

58

(3)

3.3.3 Fuzzy nonparametric regression discontinuity

61

(3)

3.3.4 Before-after (BA)

64

(1)

3.4 Treatment effect estimator with weighting

65

(7)

3.4.1 Effect on the untreated

67

(1)

3.4.2 Effects on the treated and on the population

68

(1)

3.4.3 Efficiency bounds and efficient estimators

69

(2)

3.4.4 An empirical example

71

(1)

3.5 Complete pairing with double sums

72

(7)

3.5.1 Discrete covariates

72

(2)

3.5.2 Continuous or mixed (continuous or discrete) covariates

74

(2)

3.5.3 An empirical example

76

(3)

4 Matching

79

(38)

4.1 Estimators with matching

80

(5)

4.1.1 Effects on the treated

80

(2)

4.1.2 Effects on the population

82

(2)

4.1.3 Estimating asymptotic variance

84

(1)

4.2 Implementing matching

85

(7)

4.2.1 Decisions to make in matching

85

(3)

4.2.2 Evaluating matching success

88

(2)

4.2.3 Empirical examples

90

(2)

4.3 Propensity score matching

92

(5)

4.3.1 Balancing observables with propensity score

93

(1)

4.3.2 Removing overt bias with propensity-score

93

(2)

4.3.3 Empirical examples

95

(2)

4.4 Matching for hidden bias

97

(2)

4.5 Difference in differences (DD)

99

(12)

4.5.1 Mixture of before-after and matching

99

(1)

4.5.2 DD for post-treatment treated in no-mover panels

100

(3)

4.5.3 DD with repeated cross-sections or panels with movers

103

(2)

4.5.4 Linear models for DD

105

(3)

4.5.5 Estimation of DD

108

(3)

4.6 Triple differences (TD)

111

(6)

4.6.1 TD for qualified post-treatment treated

112

(1)

4.6.2 Linear models for TD

113

(2)

4.6.3 An empirical example

115

(2)

5 Design and instrument for hidden bias

117

(30)

5.1 Conditions for zero hidden bias

117

(2)

5.2 Multiple ordered treatment groups

119

(4)

5.2.1 Partial treatment

119

(3)

5.2.2 Reverse treatment

122

(1)

5.3 Multiple responses

123

(2)

5.4 Multiple control groups

125

(4)

5.5 Instrumental variable estimator (IVE)

129

(7)

5.5.1 Potential treatments

129

(2)

5.5.2 Sources for instruments

131

(3)

5.5.3 Relation to regression discontinuity design

134

(2)

5.6 Wald estimator, IVE, and compliers

136

(11)

5.6.1 Wald estimator under constant effects

136

(2)

5.6.2 IVE for heterogenous effects

138

(1)

5.6.3 Wald estimator as effect on compliers

139

(3)

5.6.4 Weighting estimators for complier effects

142

(5)

6 Other approaches for hidden bias

147

(24)

6.1 Sensitivity analysis

147

(13)

6.1.1 Unobserved confounder affecting treatment

148

(4)

6.1.2 Unobserved confounder affecting treatment and response

152

(5)

6.1.3 Average of ratios of biased to true effects

157

(3)

6.2 Selection correction methods

160

(3)

6.3 Nonparametric bounding approaches

163

(4)

6.4 Controlling for post-treatment variables to avoid confounder

167

(4)

7 Multiple and dynamic treatments

171

(20)

7.1 Multiple treatments

171

(6)

7.1.1 Parameters of interest

172

(2)

7.1.2 Balancing score and propensity score matching

174

(3)

7.2 Treatment duration effects with time-varying covariates

177

(4)

7.3 Dynamic treatment effects with interim outcomes

181

(10)

7.3.1 Motivation with two-period linear models

181

(5)

7.3.2 G algorithm under no unobserved confounder

186

(2)

7.3.3 G algorithm for three or more periods

188

(3)

Appendix

191

(42)

A.1 Kernel nonparametric regression

191

(5)

A.2 Appendix for Chapter 2

196

(5)

A.2.1 Comparison to a probabilistic causality

196

(2)

A.2.2 Learning about joint distribution from marginals

198

(3)

A.3 Appendix for Chapter 3

201

(3)

A.3.1 Derivation for a semi-linear model

201

(1)

A.3.2 Derivation for weighting estimators

202

(2)

A.4 Appendix for Chapter 4

204

(10)

A.4.1 Non-sequential matching with network flow algorithm

204

(2)

A.4.2 Greedy non-sequential multiple matching

206

(3)

A.4.3 Nonparametric matching and support discrepancy

209

(5)

A.5 Appendix for Chapter 5

214

(7)

A.5.1 Some remarks on LATE

214

(2)

A.5.2 Outcome distributions for compliers

216

(3)

A.5.3 Median treatment effect

219

(2)

A.6 Appendix for Chapter 6

221

(5)

A.6.1 Controlling for affected covariates in a linear model

221

(3)

A.6.2 Controlling for affected mean-surrogates

224

(2)

A.7 Appendix for Chapter 7

226

(7)

A.7.1 Regression models for discrete cardinal treatments

226

(2)

A.7.2 Complete pairing for censored responses

228

(5)

References

233

(12)

Index

245

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Micro-Econometrics for Policy, Program, and Treatment Effects

Summary

Author Biography

Table of Contents

Supplemental Materials