Causal Inference

"Causality is central to the understanding and use of data; without an understanding of cause and effect relationships, we cannot use data to answer important questions in medicine and many other fields"--

Series Foreword ix
List of Examples xi
List of Methodological Topics xiii
1 The Effects Caused by Treatments 1
2 Randomized Experiments 21
3 Observational Studies: The Problem 47
4 Adjustments for Measured Covariates 67
5 Sensitivity to Unmeasured Covariates 85
6 Quasi-Experimental Devices in the Design of Observational Studies 103
7 Natural Experiments, Discontinuities, and Instruments 117
8 Replication, Resolution, and Evidence Factors 149
9 Uncertainty and Complexity in Causal Inference 159
Postscript: Key Ideas, Chapter by Chapter 175
Glossary 179
Notes 181
Bibliography 189
Further Reading 197
Index 199