Political Analysis Advance Access originally published online on January 31, 2007
Political Analysis 2007 15(3):199-236; doi:10.1093/pan/mpl013
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Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Stanford Law School, 559 Nathan Abbott Way, Stanford, CA 94305
e-mail: dho{at}law.stanford.edu
Department of Politics, Princeton University, Princeton, NJ 08544
e-mail: kimai{at}princeton.edu
Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138
Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, 624 North Broadway, Room 804, Baltimore, MD 21205
e-mail: estuart{at}jhsph.edu
e-mail: king{at}harvard.edu (corresponding author)
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.
Authors' note: Our thanks to Dan Carpenter and Jeff Koch for data; Alberto Abadie, Neal Beck, Sam Cook, Alexis Diamond, Ben Hansen, Guido Imbens, Olivia Lau, Gabe Lenz, Paul Rosenbaum, Don Rubin, and Jas Sekhon for many helpful comments; and the National Institutes of Aging (P01 AG17625-01), the National Institute of Mental Health (MH066247), the National Science Foundation (SES-0318275, IIS-9874747, SES-0550873), and the Princeton University Committee on Research in the Humanities and Social Sciences for research support. Software to implement the methods in this paper is available at http://GKing.Harvard.edu/matchit and a replication data file is available as Ho et al. (2006).
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