Statistical Models and Causal Inference: A Dialogue with the Social Sciences

by David Freedman, David Collier (Editor), Philip B. Stark (Editor)

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.

Genres: Mathematics, Science, Nonfiction

416 pages, Paperback

First published November 30, 2009

Reviews

[Rich]

Stick with Freedman's articles or his chapter in Brady and Collier's Rethinking Social Inquiry, this is basically a compendium of his previously published critiques of observational studies.

[Claus Andersen]

Thought-provoking.