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Re: st: Implementation of Propensity Score Matching, BalancingProperty


From   David Radwin <radwin@berkeley.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Implementation of Propensity Score Matching, BalancingProperty
Date   Wed, 10 Sep 2008 09:51:33 -0700

You and other Stata users interested in matching might look at this recent paper, which comes with accompanying open source software for Stata and R. Instructions for installing the software are at http://gking.harvard.edu/cem/ .

Right now I am not in any position to pass judgment on its relative strengths and weaknesses compared to -psmatch2- or other alternatives, but maybe someone else has an opinion.

David
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Stefano M. Iacus, Gary King, and Giuseppe Porro, "Matching for Causal Inference Without Balance Checking"; copy at http://gking.harvard.edu/files/abs/cem-abs.shtml.

Abstract: We address a major discrepancy in matching methods for causal inference in observational data. Since these data are typically plentiful, the goal of matching is to reduce bias and only secondarily to keep variance low. However, most matching methods seem designed for the opposite problem, guaranteeing sample size ex ante but limiting bias by controlling for covariates through reductions in the imbalance between treated and control groups only ex post and only sometimes. (The resulting practical difficulty may explain why most published applications do not check whether imbalance was reduced and so may not even be decreasing bias.) We introduce a new class "Monotonic Imbalance Bounding" (MIB) matching methods that enables one to choose a fixed level of maximum imbalance, or to reduce maximum imbalance for one variable without changing the maximum imbalance for the others. We then discuss a specific MIB method called "Coarsened Exact Matching" (CEM) which, unlike most existing approaches, also explicitly bounds through ex ante user choice both the degree of model dependence and the treatment effect estimation error, eliminates the need for a separate procedure to restrict data to common support, meets the congruence principle, is robust to measurement error, works well with modern methods of imputation for missing data, is computationally efficient even with massive data sets, and is easy to understand and use. This method can improve causal inferences in a wide range of applications, and may be preferred for simplicity of use even when it is possible to design superior methods for particular problems. We also make available open source software which implements all our suggestions.

At 10:03 AM -0600 9/10/08, Pena,Anita wrote:

Dear Statalist,
I am struggling with implementing propensity score matching. Specifically, I am stuck with the balancing property and differences between pscore followed by att*, psmatch2 with pstest, and nnmatch. I have tried several specifications using pscore for which the balancing property test consistently fails. Using pstest after psmatch2, however, balancing seems to be satisfied for the same specifications that fail under pscore. I would greatly appreciate if someone who has recently implemented propensity score matching in STATA could provide pointers on how to get balancing and also any insight on the advantages/disadvantages of the three alternative commands.
Many thanks!
----------------------------------------
Dr. Anita Alves Pena
Department of Economics
Colorado State University
<http://lamar.colostate.edu/~aalves>

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David Radwin // radwin@berkeley.edu
Office of Student Research, University of California, Berkeley
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