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Re: st: Re: psmatch2 question


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: psmatch2 question
Date   Wed, 25 Aug 2010 05:51:43 -0400

--

Anna,

First, I must apologize. I showed a method for estimating the ATT
(weighting by propensity scores) which is different from the one that
-psmatch2- uses (matching on propensity scores). So my prgoram. does
not apply to your analysis. The -help- for -psmatch2- illustrates a
bootstrap approach to estimating the standard error, although it
states that it is "unclear whether the bootstrap is valid in this
context."

Also, there is literature (see page 26 of Austin Nichol's presentation
http://repec.org/dcon09/dc09_nichols.pdf) which suggests that
estimated propensity scores might be more accurate than the unknown
true scores; if so, then standard errors which consider the estimated
scores as known might be better than the bootstrapped standard errors!
So there is apparently no right answer. I think that a conservative
approach would be to use the -bootstrap- technique shown in the
-psmatch2- help, followed by "estat bootstrap, all" to get confidence
intervals for ATT.

ATT is one of several methods for describing the causal effect of a
treatment: To quote Austin's presentation (p 15): "For evaluating the
effect of a treatment/intervention/program, we may want to estimate the
ATE for participants (the average treatment effect on the treated, or
ATT) or for potential participants who are currently not treated (the
average treatment effect on controls, or ATC), or the ATE across the
whole population (or even for just the sample under study)."

Best wishes

Steve

-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

On Tue, Aug 24, 2010 at 7:23 PM, anna bargagliotti <abargag@yahoo.com> wrote:
> Thank you for your insights about bootstrapping.  I wiill try adjusting  your
> code to my situation to reproduce the T-stat and compute the p-value.
>
> I am, however, still confused about two very simple things:
> 1.  What is the T-stat for the ATT actually telling us?  Is this the T-stat for
> the comparison of treatment vs control matched groups?
> 2.  How do we determine if there is a treatment effect?
>
>
>
>
>
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