Ho, Daniel. E., Kosuke Imai, Gary King and Elizabeth A. Stuart. (2007). ``Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.'' Political Analysis, Vol. 15, No. 3 (Summer), pp. 199-236.



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.

  Related Information

This paper has won the Warren Miller Prize for the best paper published in Political Analysis in 2008.
In October 2008, it also was recognized by Thomson Reuters' ScienceWatch as a fast breaking paper for the article with the largest percentage increase in citations among those in the top 1% of total citations across the social sciences in the last two years.
You may also be interested in Imai, Kosuke, Gary King, and Elizabeth A. Stuart. (2008). ``Misunderstandings among Experimentalists and Observationalists about Causal Inference.'' Journal of the Royal Statistical Society, Series A, Vol. 171, No. 2 (April), pp. 481-502.


Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. ``MatchIt: Nonparametric Preprocessing for Parametric Causal Inferece.''

  Replication archive

Ho, Daniel E.; Imai, Kosuke; King, Gary; and Stuart, Elizabeth A., (2006), "Replication Data Set for 'Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference'", hdl:1902.1/YVDZEQIYDS http://id.thedata.org/hdl%3A1902.1%2FYVDZEQIYDS ; UNF:3:QV0mYCd8eV+mJgWDnYct5g== Murray Research Archive [distributor(DDI)]

© Kosuke Imai
  Last modified: Tue Jul 14 21:41:32 EDT 2009