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Re: re:st: pscore question
Dan Kimmel <email@example.com>
Re: re:st: pscore question
Wed, 16 Feb 2011 09:01:42 -0600
Thanks for your suggestions re: my pscore question. I am now
encountering another problem, which was (indeed) my reason for not
simply using psmatch2 in the first place: does anyone know if there is
a way to use psmatch2 (or some other propensity score module) to model
a nominal- or ordinal-scale outcome? I am attempting to predict the
effect of exposure to violence in school on students' health outcomes,
where health is measured with a simple 5-category subjective response.
Daniel M. Kimmel
Department of Sociology
University of Chicago
On Thu, Feb 10, 2011 at 4:05 PM, Alistair Windsor
> 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"<firstname.lastname@example.org>
>> 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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