Statistical Models and Causal Inference

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.