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

Posted: 12 Aug 2009

See all articles by Daniel E. Ho

Daniel E. Ho

Stanford Law School

Kosuke Imai

Princeton University - Department of Political Science

Gary King

Harvard University

Elizabeth A. Stuart

Johns Hopkins University - Bloomberg School of Public Health

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Abstract

Although published works 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? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. 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 the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.

Suggested Citation

Ho, Daniel E. and Imai, Kosuke and King, Gary and Stuart, Elizabeth A., Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, Vol. 15, Issue 3, pp. 199-236, 2007, Available at SSRN: https://ssrn.com/abstract=1447740 or http://dx.doi.org/10.1093/pan/mpl013

Daniel E. Ho (Contact Author)

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305-8610
United States
650-723-9560 (Phone)

HOME PAGE: http://dho.stanford.edu

Kosuke Imai

Princeton University - Department of Political Science ( email )

Corwin Hall
Princeton, NJ 08544-1012
United States

Gary King

Harvard University ( email )

1737 Cambridge St.
Institute for Quantitative Social Science
Cambridge, MA 02138
United States
617-500-7570 (Phone)

HOME PAGE: http://gking.harvard.edu

Elizabeth A. Stuart

Johns Hopkins University - Bloomberg School of Public Health ( email )

615 North Wolfe Street
Baltimore, MD 21205
United States

HOME PAGE: http://www.biostat.jhsph.edu/~estuart

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