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From |
Paul <paulburk314@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: pweights, propensity scores |

Date |
Fri, 18 Jan 2013 09:17:47 -0500 |

Thank you very much for your reply. There seem to be a few problems with that solution though: I don't wan aweights, which stata is assuming with the [w=w]. This shouldn't matter for the point estimates though, so it's still a useful exercise. Also, using sum over treated and untreated seperately can't give me the formula I want, since I need N^-1 in front of the summand. Your solution does at least give me insight into what pweight is doing in the reg command, which, contrary to the stata journal article I cite below, seems incorrect. Thanks. On Fri, Jan 18, 2013 at 3:59 AM, Christophe Kolodziejczyk <ck.statalist@gmail.com> wrote: > Hi Paul > I think you have to compute the weighted mean separately for the > different samples, i.e the first weighted sum should be divided by the > number of treated and the second sum should be divided by the number > of controls. > > > logit treatment X > predict p > > gen w = cond(treatment,1/p,1/(1-p)) > > reg y treatment [w=w] > > sum y if treatment [w=w] > local m1 = `r(mean)' > sum y if !treatment [w=w] > local m0 = `r(mean)' > > di "ATE = " `=`m1'-`m0'' > > > > Best > Christophe > > > 2013/1/17 Paul <paulburk314@gmail.com>: >> Hi all, >> >> I'm using propensity scores to estimate treatment effects, where >> treatment is exogenous conditional on the propensity score. I'm using >> an estimator from Wooldridge's 2010 text book, which is also discussed >> in The Stata Journal (2008) 8, Number 3, pp. 334–353. >> >> Specifically, the treatment effect is estimated using (1/N) sum >> (T*Y/p) - (1/N) sum ((1-T)*Y/(1-p). >> >> According to the Stata Journal article, this can be estimated using a >> regression with pweights equal to the "inverse of the treatment >> probability deﬁned using the >> propensity score." However, when I use just the sum of the weighted >> variables, I get a different answer from the regression result. I'm >> not terribly familiar with pweights, so I could be making some dumb >> mistake. >> >> Below is my code. Does anyone know what I'm doing wrong, or what the >> correct way to implement this method is? >> >> Thanks, >> Paul >> >> /* Regression using pweights */ >> gen ipw=1/p_x if treated==1 >> replace ipw=1/(1-p_x) if treated==0 >> >> reg y treated [pweight=ipw] >> >> /* IPTW one variable */ >> gen w1=((treated-p_x)/(p_x*(1-p_x))) >> gen w1_y=w1 *y >> >> sum w1_y >> >> /* IPTW two variables */ >> gen w2a_y=y*treated/p_x >> gen w2b_y=y*(1-treated)/(1-p_x) >> >> foreach type in a b{ >> sum w2`type'_y >> local mean_w2`type' =r(mean) >> } >> >> di `mean_w2a'-`mean_w2b' >> >> /* IPTW two variables weights sum to one */ >> bysort treated: egen w_ipw=total(ipw) >> gen w3a_y=(1/w_ipw)*y*treated/p_x >> gen w3b_y=(1/w_ipw)*y*(1-treated)/(1-p_x) >> >> foreach type in a b{ >> sum w3`type'_y >> local mean_w3`type' =r(mean) >> } >> >> di `mean_w3a'-`mean_w3b' >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ > > > > -- > Christophe Kolodziejczyk > Research Fellow > > AKF, Anvendt KommunalForskning > Danish Institute of Governmental Research > Købmagergade 22 > DK-1150 København K > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: pweights, propensity scores***From:*William Buchanan <william@williambuchanan.net>

**References**:**st: pweights, propensity scores***From:*Paul <paulburk314@gmail.com>

**Re: st: pweights, propensity scores***From:*Christophe Kolodziejczyk <ck.statalist@gmail.com>

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