Fundamentals of Causal Inference with R

249 Pages·2021·7.92 MB·English

by  Babette A. Brumback

Fundamentals of
Causal Inference
with R
CHAPMAN & HALL/CRC
Texts in Statistical Science Series
Joseph K. Blitzstein, Harvard University, USA
Julian J. Faraway, University of Bath, UK
Martin Tanner, Northwestern University, USA
Jim Zidek, University of British Columbia, Canada
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Fundamentals of
Causal Inference
with R
Babette A. Brumback
F
irst edition published 2022
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ISBN: 9780367705091 (pbk)
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To my parents

Contents
Preface xi
1 Introduction 1
1.1 A Brief History . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Data Examples . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Mortality Rates by Country . . . . . . . . . . . . . . . 6
1.2.2 National Center for Education Statistics . . . . . . . . 8
1.2.3 Reducing Alcohol Consumption . . . . . . . . . . . . . 9
1.2.3.1 The What-If? Study . . . . . . . . . . . . . . 9
1.2.3.2 The Double What-If? Study . . . . . . . . . 10
1.2.4 General Social Survey . . . . . . . . . . . . . . . . . . 13
1.2.5 A Cancer Clinical Trial . . . . . . . . . . . . . . . . . 15
1.3 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Conditional Probability and Expectation 19
2.1 Conditional Probability . . . . . . . . . . . . . . . . . . . . . 19
2.2 Conditional Expectation and the Law of Total Expectation . 21
2.3 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4 Sampling Distributions and the Bootstrap . . . . . . . . . . 29
2.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 Potential Outcomes and the Fundamental Problem of Causal
Inference 37
3.1 Potential Outcomes and the Consistency Assumption . . . . 38
3.2 Circumventing the Fundamental Problem of Causal Inference 39
3.3 Effect Measures . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4 Effect-Measure Modification and Causal Interaction 59
4.1 Effect-Measure Modification and Statistical Interaction . . . 60
4.2 Qualitative Agreement of Effect Measures in Modification . . 71
4.3 Causal Interaction . . . . . . . . . . . . . . . . . . . . . . . . 75
4.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
vii
viii Contents
5 Causal Directed Acyclic Graphs 81
5.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.3 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
6 Adjusting for Confounding: Backdoor Method via
Standardization 99
6.1 Standardization via Outcome Modeling . . . . . . . . . . . . 99
6.1.1 Average Effect of Treatment on the Treated . . . . . . 105
6.1.2 Standardization with a Parametric Outcome Model. . 110
6.2 Standardization via Exposure Modeling . . . . . . . . . . . . 113
6.2.1 Average Effect of Treatment on the Treated . . . . . . 115
6.2.2 Standardization with a Parametric Exposure Model . 116
6.3 Doubly Robust Standardization . . . . . . . . . . . . . . . . 123
6.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7 Adjusting for Confounding: Difference-in-Differences
Estimators 133
7.1 Difference-in-Differences (DiD) Estimators with Linear,
Loglinear, and Logistic Models . . . . . . . . . . . . . . . . . 135
7.1.1 DiD Estimator with a Linear Model . . . . . . . . . . 136
7.1.2 DiD Estimator with a Loglinear Model. . . . . . . . . 137
7.1.3 DiD Estimator with a Logistic Model . . . . . . . . . 137
7.2 Comparison with Standardization . . . . . . . . . . . . . . . 138
7.3 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
8 Adjusting for Confounding: Front-Door Method 147
8.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
8.2 Theory and Method . . . . . . . . . . . . . . . . . . . . . . . 150
8.3 Simulated Example . . . . . . . . . . . . . . . . . . . . . . . 152
8.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
9 Adjusting for Confounding: Instrumental Variables 157
9.1 Complier Average Causal Effect and Principal Stratification 158
9.2 Average Effect of Treatment on the Treated and Structural
Nested Mean Models . . . . . . . . . . . . . . . . . . . . . . 160
9.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
9.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
10 Adjusting for Confounding: Propensity-Score Methods 175
10.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
10.2 Using the Propensity Score in the Outcome Model . . . . . . 179
10.3 Stratification on the Propensity Score . . . . . . . . . . . . . 180
10.4 Matching on the Propensity Score . . . . . . . . . . . . . . . 181
10.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Contents ix
11 Gaining Efficiency with Precision Variables 187
11.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
11.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
11.3 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
12 Mediation 195
12.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
12.2 Traditional Parametric Methods . . . . . . . . . . . . . . . . 199
12.3 More Examples . . . . . . . . . . . . . . . . . . . . . . . . . 200
12.4 Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
13 Adjusting for Time-Dependent Confounding 209
13.1 Marginal Structural Models . . . . . . . . . . . . . . . . . . . 210
13.2 Structural Nested Mean Models . . . . . . . . . . . . . . . . 212
13.3 Optimal Dynamic Treatment Regimes . . . . . . . . . . . . . 215
13.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Appendix 223
Bibliography 225
Index 233