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re: re:st: pscore question

From   Alistair Windsor <>
Subject   re: re:st: pscore question
Date   Thu, 10 Feb 2011 16:05:04 -0600

Propensity score reweighting is a possibility but often it does not adequately correct for the covariate imbalance. See for example "Propensity Score Analysis: Statistical Methods and Applications" by Guo and Fraser. Reweighting does allow easy use of regression for further analysis unlike methods like full matching.

Associated to psmatch2 is pstest which looks at covariate imbalance after matching. I also like to run logit/probit regressions on the treatment over the combined treated and pseudo control groups and look for joint vanishing of the non-constant terms using a Wald or likelihood ratio test.

One further option is to look into optmatch for R. This performs optimal matching using a variety of schemes.

The best bet is to try several techniques and see which work best for your purposes.



On 2/10/11 1:33 AM, statalist-digest wrote:
Date: Wed, 9 Feb 2011 10:23:19 -0500
From: "Ariel Linden. DrPH"<>
Subject: re:st: pscore question


If you already have a propensity score estimated, you can use psmatch2" (a
user written program by Edwin Leuven and Barbara Sianesi). This program
allows you to use an existing propscore and it will conduct the necessary
tests of balance on covariates.

You also asked about blocking - there is another program that naturally
"blocks", but is flexible enough to allow you to generate your own blocks.
This program is called "cem" [Coarsened Exact Matching], a user-written
program by Matthew Blackwell and Gary King at Harvard.

Given what you are trying to do, I would probably prefer to use propensity
score-based weighting over matching. Austin Nichols wrote a nice paper on
that " Erratum and discussion of propensity-score reweighting" in
Stata Journal  8(4):532--539

I hope this helps, and good luck!


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