David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a shoe leather methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with skepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor low-tech approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman ‘s views.
About the Author
David Collier is Robson Professor of Political Science at University of California, Berkeley. His current work focuses on conceptualization and measurement and on causal inference in qualitative and multi-method research. He is co-author of Rethinking Social Inquiry: Diverse Tools, Shared Standards and co-editor of two recent volumes: The Oxford Handbook of Political Methodology and Concepts and Method in Social Science. A central concern of his research is with the contribution of detailed case knowledge to causal inference and concept formation.
Jasjeet S. Sekhon is Associate Professor of Political Science at University of California, Berkeley. His current research focuses on methods for causal inference in observational and experimental studies and evaluating social science, public health, and medical interventions. Professor Sekhon has done research on elections, voting behavior, and public opinion in the United States; multivariate matching methods for causal inference; machine learning algorithms for irregular optimization problems; robust estimators with bounded influence functions; health economic cost effectiveness analysis; and the philosophy and history of inference and statistics in the social sciences.
Philip B. Stark is Professor of Statistics at University of California, Berkeley. His research centers on inference problems, focusing on quantifying the uncertainty in inferences that rely on numerical models of complex physical systems. Professor Stark has done research on the Big Bang, causal inference, the US census, chemical spectroscopy, earthquake prediction, election auditing, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics, the seismic structure of the Earth and Sun, and spectrum estimation. He has served as an expert witness on statistics for government and industry in state and federal courts.
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