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re: re:st: pscore question
From
Alistair Windsor <[email protected]>
To
[email protected]
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.
Yours,
Alistair
On 2/10/11 1:33 AM, statalist-digest wrote:
Date: Wed, 9 Feb 2011 10:23:19 -0500
From: "Ariel Linden. DrPH"<[email protected]>
Subject: re:st: pscore question
Daniel,
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!
Ariel
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