Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference


15 Political Analysis 199 (2007).



Daniel E. Ho, Kosuke Imai, Gary King, & Elizabeth A. Stuart


Abstract

Although articles rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author's favorite hypothesis? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this young and fast-growing methodological literature are often grossly misinterpreted in applications. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply whatever familiar parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.


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