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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: propensity score and mills ratio |

Date |
Tue, 28 Oct 2008 17:17:07 +0100 |

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? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: propensity score and mills ratio***From:*"francesca.modena" <francesca.modena@email.unitn.it>

**Re: st: RE: propensity score and mills ratio***From:*"Austin Nichols" <austinnichols@gmail.com>

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