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Re: st: RE: Propensity score matching -balancing property
From
Nyasha Tirivayi <[email protected]>
To
[email protected]
Subject
Re: st: RE: Propensity score matching -balancing property
Date
Sat, 15 Jan 2011 19:10:28 +0100
Dear Daniel
Thank you for your response.
For PSM I am assessing outcomes for different units of observation
within the same dataset i.e. about 400 observations for household
outcomes and around 1600 for individual outcomes. Would you consider
this to be small changes in sample size? A
The double robust estimator you refer to, is it for matching or regression?
Kindly reply
Regards
N.Tirivayi
Maastricht University
On Sat, Jan 15, 2011 at 5:57 PM, Millimet, Daniel <[email protected]> wrote:
> The outcome has nothing to do with balancing since it does not factor into the balancing tests (only the p-score and Xs matter). The difference, as you note, is that the sample changes across outcomes, and these explains your changing balancing results.
>
> I would be skeptical about why the balancing test is that sensitive to (presumably small) changes in sample size. This suggests that one should be cautious claiming that the Xs are balanced in the first chapter.
>
> In light of this, as well as based on work I (with Tchernis in J Bus & Eco Stats) and other have done on the benefits of over-specifying the p-score eqtn, I would err on the side of using the least parsimonious specification for all outcomes.
>
> You might also try other estimators in addition to matching to assess robustness, such as a doubly robust estimator.
>
> ****************************************************
> Daniel L. Millimet, Professor
> Department of Economics
> Box 0496
> SMU
> Dallas, TX 75275-0496
> phone: 214.768.3269
> fax: 214.768.1821
> web: http://faculty.smu.edu/millimet
> ****************************************************
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On Behalf Of Nyasha Tirivayi
> Sent: Friday, January 14, 2011 9:46 PM
> To: [email protected]
> Subject: st: Propensity score matching -balancing property
>
> Hello
>
> I have a question concerning psmatch2 and general propensity score matching:
>
> The propensity score model I have used to analyse the first outcome
> for my first chapter of research does not satisfy the balancing
> property when I apply it to the other outcomes to be presented in
> later chapters. Should I use the propensity score model I have used in
> the first paper throughout the next chapters for all the outcomes,
> even if it does not balance all the time? Or each outcome might
> require a separate propensity score model with maybe different
> covariates?
>
> Each outcome also has different number of observations. Does this
> support the use of separate PSM models?
>
> Kindly respond
>
> N.Tirivayi
> Maastricht university
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