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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: psmatch2 question |

Date |
Wed, 25 Aug 2010 17:01:10 -0400 |

anna bargagliotti <abargag@yahoo.com>: No, you do not use the pscore as a pweight; read the article I linked to: "The pair of commands generating weights [for the ATT estimate] can be replaced by the single command g w=cond(_tr,1,_ps/(1-_ps)) [where _tr is a treatment dummy and _ps the propensity score]..." On Wed, Aug 25, 2010 at 3:53 PM, anna bargagliotti <abargag@yahoo.com> wrote: > Thanks Austin and Steve! Let me restate in my own words to see if I > understand.. > > My goal is to compare the gpas of treatments vs a matched control. The psmatch2 > command computed propensity score for each student. In order to produce a more > accurate t-stat than the ATT t-stat given by the psmatch2 command, I can regress > gpa on the treatment dummy and a set of X variables using the pscore as a > pweight. I do this for the whole sample (ie, those students that were treated, > those that were matched, and those that were unmatched). > This will produce a t-stat on the treatment dummy which will in turn give me the > wanted comparison. > > Thank you very much for your help! This has been great! > > > > ----- Original Message ---- > From: Austin Nichols <austinnichols@gmail.com> > To: statalist@hsphsun2.harvard.edu > Sent: Wed, August 25, 2010 9:07:22 AM > Subject: Re: st: Re: psmatch2 question > > Steve, Anna, et al.-- > The bootstrap is not a priori a good idea: http://www.nber.org/papers/t0325 > > But if you use nonparametric propensity scores, or equivalently a > logit with only mutually exclusive exhaustive dummies as explanatory > variables, and reweight instead of matching 1:1 or somesuch, you will > be better off in many ways; see e.g. > Hirano, K., G. Imbens, and G. Ridder. (2003). “Efficient Estimation of > Average Treatment Effects Using the Estimated Propensity Score,” > Econometrica, 71(4): 1161-1189. > > The t-stat produced by -psmatch2- is not particularly reliable, > compared to one produced by a double-robust regression, say, where you > regress the outcome on treatment and other explanatory variables using > weights based on propensity scores. But the t-stat on the ATT is > intended to guide you to reject or fail to reject the hypothesis that > the effect of treatment on those who received treatment is zero. If > you decide to bootstrap, save each estimated ATT and its SE and see > how the matching estimator's SE compares to the observed standard > deviation of estimates; then do the same with the nonparametric > propensity score reweighting estimator and you will probably decide > not to match but to reweight. > > A minor point: estimated propensity scores are never "more accurate" > than the unknown true scores, but even if you knew the true propensity > scores, you could get more efficient estimates in many cases by > throwing that information away and estimating propensity scores. This > is why computing a SE as if the propensity scores are fixed and known > is reasonable. > > Instead of the presentation, you may want the papers: > http://www.stata-journal.com/article.html?article=st0136 > http://www.stata-journal.com/article.html?article=st0136_1 > http://www-personal.umich.edu/~nicholsa/ciwod.pdf > http://www-personal.umich.edu/~nicholsa/erratum.pdf > > On Wed, Aug 25, 2010 at 5:51 AM, Steve Samuels <sjsamuels@gmail.com> wrote: >> -- >> >> Anna, >> >> First, I must apologize. I showed a method for estimating the ATT >> (weighting by propensity scores) which is different from the one that >> -psmatch2- uses (matching on propensity scores). So my program does >> not apply to your analysis. The -help- for -psmatch2- illustrates a >> bootstrap approach to estimating the standard error, although it >> states that it is "unclear whether the bootstrap is valid in this >> context." >> >> Also, there is literature (see page 26 of Austin Nichols's presentation >> http://repec.org/dcon09/dc09_nichols.pdf) which suggests that >> estimated propensity scores might be more accurate than the unknown >> true scores; if so, then standard errors which consider the estimated >> scores as known might be better than the bootstrapped standard errors! >> So there is apparently no right answer. I think that a conservative >> approach would be to use the -bootstrap- technique shown in the >> -psmatch2- help, followed by "estat bootstrap, all" to get confidence >> intervals for ATT. >> >> ATT is one of several methods for describing the causal effect of a >> treatment: To quote Austin's presentation (p 15): "For evaluating the >> eﬀect of a treatment/intervention/program, we may want to estimate the >> ATE for participants (the average treatment eﬀect on the treated, or >> ATT) or for potential participants who are currently not treated (the >> average treatment eﬀect on controls, or ATC), or the ATE across the >> whole population (or even for just the sample under study)." >> >> Best wishes >> >> Steve >> > >> On Tue, Aug 24, 2010 at 7:23 PM, anna bargagliotti <abargag@yahoo.com> wrote: >>> Thank you for your insights about bootstrapping. I wiill try adjusting your >>> code to my situation to reproduce the T-stat and compute the p-value. >>> >>> I am, however, still confused about two very simple things: >>> 1. What is the T-stat for the ATT actually telling us? Is this the T-stat >>for >>> the comparison of treatment vs control matched groups? >>> 2. How do we determine if there is a treatment effect? > * * 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: Anyone has the Stata code for National Nursing Home Survey of various years***From:*"G. Dai" <dgecon@gmail.com>

**st: RE: Anyone has the Stata code for National Nursing Home Survey of various years***From:*Nick Cox <n.j.cox@durham.ac.uk>

**st: psmatch2 question***From:*anna bargagliotti <abargag@yahoo.com>

**st: Re: psmatch2 question***From:*"Joseph Coveney" <jcoveney@bigplanet.com>

**Re: st: Re: psmatch2 question***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: Re: psmatch2 question***From:*anna bargagliotti <abargag@yahoo.com>

**Re: st: Re: psmatch2 question***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: Re: psmatch2 question***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Re: psmatch2 question***From:*anna bargagliotti <abargag@yahoo.com>

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