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Authors' Committee

Chair:

Matt Blackwell (Gov)

Members:

Martin Andersen (HealthPol)
Kevin Bartz (Stats)
Deirdre Bloome (Social Policy)
John Graves (HealthPol)
Rich Nielsen (Gov)
Maya Sen (Gov)
Gary King (Gov)

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Alberto Abadie, Lee Fleming, Adam Glynn, Guido Imbens, Gary King, Arthur Spirling, Jamie Robins, Don Rubin, Chris Winship

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« October 17, 2011 | Main | October 31, 2011 »

23 October 2011

App Stats: Nielsen on "Comparative Effectiveness of Matching Methods for Causal Inference"

We hope you can join us this Wednesday, October 26, 2011 for the Applied Statistics Workshop. Rich Nielsen, a Ph.D. candidate from the Department of Government at Harvard University, will present a paper entitled "Comparative Effectiveness of Matching Methods for Causal Inference". A light lunch will be served at 12 pm and the talk will begin at 12.15.

"Comparative Effectiveness of Matching Methods for Causal Inference"
Rich Nielsen
Government Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, October 26th, 2011 12.00 pm

Abstract:

Matching is an increasingly popular method of causal inference in observational data, but following methodological best practices has proven difficult for applied researchers. We address this problem by providing a simple graphical approach for choosing among the numerous possible matching solutions generated by three methods: the venerable "Mahalanobis Distance Matching" (MDM), the commonly used "Propensity Score Matching" (PSM), and a newer approach called "Coarsened Exact Matching" (CEM). In the process of using our approach, we also discover that PSM often approximates random matching, both in many real applications and in data simulated by the processes that fit PSM theory. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus PSM) can often degrade inferences relative to not matching at all. We find that MDM and CEM do not have this problem, and in practice CEM usually outperforms the other two approaches. However, with our comparative graphical approach and easy-to-follow procedures, focus can be on choosing a matching solution for a particular application, which is what may improve inferences, rather than the particular method used to generate it.

The paper is joint work with Gary King, Carter Coberley, James E. Pope, and Aaron Wells.

Posted by Konstantin Kashin at 10:54 PM