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Re: st: RE: propensity score and mills ratio


From   "Austin Nichols" <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: propensity score and mills ratio
Date   Tue, 28 Oct 2008 12:22:56 -0400

Sorry--forgot to put
predict p
but no, kdens is on SSC.

On Tue, Oct 28, 2008 at 12:17 PM, Martin Weiss <martin.weiss1@gmx.de> wrote:
> Just as an aside: Where does "p" come from in this code? Should there be a
> -predict p- after the -probit-? Is -kdens- supposed to be an abbreviation
> for -kdensity-?
>
>
> HTH
> Martin
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Austin Nichols
> Sent: Tuesday, October 28, 2008 4:47 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: RE: propensity score and mills ratio
>
> francesca.modena--
>
> Note it is not really a "sample selection problem" so much as a
> treatment (T=1) selection problem. Matching or an IV approach (like
> the -treatreg- model proposed in the original post) each requires
> different assumptions. Propensity score matching requires that T is
> essentially random conditional on X or the propensity score, and that
> 0<p(T|X)<1 where we need to emphasize (very) strict inequality; of
> course estimated p is never 0 or 1 but if the density is positive or
> even if the slope of the density is positive at 0 or 1 you may have
> problems.  IV (or treatreg) requires that components of X not also in
> Z (Z is what you called your included instruments, usually called X)
> do not have a direct impact on outcomes (your X can affect outcomes in
> a matching model), but strongly predict T (note that if your X too
> strongly predicts T you will fail the 0<p<1 test in propensity score
> matching; what is bad for matching is good for IV).  See also
> http://pped.org/stata/erratum.pdf and references therein.
>
> ps. Thanks for the plug, Martin.
>
> pps. to see what I mean about the density of estimated p being
> positive at the boundaries try
>
> use http://pped.org/stata/card
> g c=educ>=16
> probit c fath moth nearc2 nearc4 south66 smsa66 black
> kdens p if c==1, ll(0) ul(1) bw(.1)
> kdens p if c==0, ll(0) ul(1) bw(.1)
> psmatch2 c fath moth nearc2 nearc4 south66 smsa66 black, out(lwage)
> psgraph, bin(50)
> treatreg lwage south66 smsa66 black, treat(c=fath moth nearc2 nearc4
> south66 smsa66 black)
>
> (note kdens and psmatch2 are on SSC).
>
> On Tue, Oct 28, 2008 at 8:23 AM, Martin Weiss <martin.weiss1@gmx.de> wrote:
>> http://www.stata-journal.com/article.html?article=st0136
>
>> -----Original Message-----
>> From: francesca.modena
>> Dear all,
>> This is a classical problem of treatment effect.
>> I have two outcomes:
>> Y1i: the outcome of unit i if i were exposed to the treatment (T=1)
>> Y0i: the outcome of unit i if i were not exposed to the treatment (T=0)
>> I want to regress Y1i on a set of characteristics Z. OLS regression of Y1i
>> on Z can be biased because of sample selection problem.
>>
>> Let us assume that the probability of being exposed to the treatment can
> be
>> described by a probit equation
> Pr(T)=f(X)
>
> --> help treatreg
>
>> Another procedure to deal with selection bias is the propensity score
>> matching.
>
> --> findit nnmatch and findit psmatch2
>
>> What is the difference between the two procedures? Can I use both mills
>> ratio and propensity score to deal with selection problems?
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