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

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

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Title: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Author: Ho, Daniel E.; Imai, Kosuke; King, Gary; Stuart, Elizabeth

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Citation: Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth Stuart. 2007. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15(3): 199-236.
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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 \emph{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.
Published Version: doi:10.1093/pan/mpl013
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4214880

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  • FAS Scholarly Articles [6464]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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