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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: deriving a bootstrap estimate of a difference between two weighted regressions |

Date |
Wed, 4 Aug 2010 08:01:26 -0400 |

My formula for the weighted mean above was wrong, because I based it on the wrong weights for the average treatment effects. I can fix the formula, but since you must bootstrap anyway, I recommend Stas's approach. Steve On Tue, Aug 3, 2010 at 3:37 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > On Tue, Aug 3, 2010 at 1:20 PM, Ariel Linden, DrPH > <ariel.linden@gmail.com> wrote: >> Thank you Stas and Steve for your comments! >> >> When I stated that the first model's weight would be ATT and the next ATC, >> it was already after running the propensity score model and establishing the >> weights for each subject: >> ATT = cond(treatvar, 1, propvar/(1- propvar)), and >> ATC = cond(treatvar, (1-propvar)/propvar, 1) >> >> Under these conditions, there should be no negative weights, so that is not >> a concern. > > The negative weights would come out of Steve's suggestion to entertain > the difference in weights (as that's what your procedure boils down > to). > >> I am thinking that the code would look something like this, but I would >> appreciate your input: >> >> 1. bootstrap _b[treatvar] from first regression with [pw=ATT] >> 2. save 10,000 samples to file (or tempfile) >> 3. bootstrap _b[treatvar] from second regression with [pw=ATC] >> 4. save 10,000 samples to file (or tempfile) >> 5. gen difference = treatvar1-treatvar2 >> 6. bootstrap r(mean): sum difference, to get bootstrapped CIs >> >> Does this make sense? > > 1-2 will produce something very similar to the _se[treatvar] in your > basic regression with ATT weights (probably with -robust- option), and > 3-4 will produce something very similar to _se[treatvar] in the > regression with ATC weights. I outlined the code for you in the > previous message -- you need to bootstrap the whole estimation > procedure = { the propensity regression (leading to the weights) + two > main regressions with two sets of weights }. In other words, for each > bootstrap sample, you would need to run everything in the curly > brackets to produce your "difference" estimate. > > I cannot comment on the scientific validity of this procedure; other > people more knowledgeable in treatment effect estimation could do > that. > > -- > Stas Kolenikov, also found at http://stas.kolenikov.name > Small print: I use this email account for mailing lists only. > > * > * 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/ > -- Steven Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 * * 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**:**Re: st: deriving a bootstrap estimate of a difference between two weighted regressions***From:*"Ariel Linden, DrPH" <ariel.linden@gmail.com>

**Re: st: deriving a bootstrap estimate of a difference between two weighted regressions***From:*Stas Kolenikov <skolenik@gmail.com>

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