Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R (Paperback)

1 Introduction 1
2 Causality and No Causality 11
3 Social Experiments and Linear Regression 19
4 Selection on Observables 65
5 Casual Machine Learning 137
6 Instrumental Variables 169
7 Difference-in-Differences 195
8 Synthetic Controls 219
9 Regression Discontinuity, Kink, and Bunching Designs 231
10 Partial Identification and Sensitivity Analysis 255
11 Treatment Evaluation under Interference Effects 271
12 Conclusion 285
References 287
Index 311