"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"--
"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
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
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