"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"--
Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is the author of Observation and Experiment: An Introduction to Causal Inference, Design of Observational Studies, Observational Studies, and Replication and Evidence Factors in Observational Studies.
Inhaltsangabe
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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