Statistical Models and Causal Inference

Abstract

This volume collects twenty of David A. Freedman’s most accessible and influential papers on the use and limits of statistical modeling in social science, policy, law, and epidemiology. Through this collection, Freedman offers an integrated synthesis of his views on causal inference. He explores the foundations and limitations of statistical modeling and evaluates research in political science, public policy, law, and epidemiology.

Freedman argues that many new technical approaches to statistical modeling constitute not progress, but regress, and he shows why these methods are not reliable. Instead, Freedman advocates “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. Freedman offers here a definitive synthesis of his approach.

Keywords: social science methodology, causal inference, foundations of statistics, statistical models