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Statistical Models and Causal Inference - A Dialogue with the Social Sciences (Hardcover): David A Freedman Statistical Models and Causal Inference - A Dialogue with the Social Sciences (Hardcover)
David A Freedman; Edited by David Collier, Jasjeet S. Sekhon, Philip B. Stark
R2,296 Discovery Miles 22 960 Ships in 10 - 15 working days

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 scepticism, 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.

Statistical Models and Causal Inference - A Dialogue with the Social Sciences (Paperback): David A Freedman Statistical Models and Causal Inference - A Dialogue with the Social Sciences (Paperback)
David A Freedman; Edited by David Collier, Jasjeet S. Sekhon, Philip B. Stark
R1,334 Discovery Miles 13 340 Ships in 10 - 15 working days

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 scepticism, 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.

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