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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? > * > * 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/ > * * 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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