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From |
William Buchanan <william@williambuchanan.net> |

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

Subject |
Re: st: pweights, propensity scores |

Date |
Fri, 18 Jan 2013 07:10:52 -0800 |

Hi Paul, If you go to the UCLA website for the book Methods Matter (Murnane and Willett, 2011), there are a few different approaches for propensity score analysis. The approach suggested there uses analytical weights. You could also check out several user-written packages that provide a much more robust set of tools for PSA (e.g., different matching algorithms, diagnostics of the matching, etc...) HTH, Billy Murnane, R., & Willett, J. (2011). Methods matter: improving causal inference in educational and social science research. New York, NY: Oxford University Press Sent from my iPhone On Jan 18, 2013, at 6:17, Paul <paulburk314@gmail.com> wrote: > 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/ * * 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/

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

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

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

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