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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 |
Mon, 2 Aug 2010 10:45:23 -0400 |

I didn't notice that the weights could be negative. Thanks for catching that, Stas! Observations with negative pweights and aweights will be excluded. You'll have to compute the weighted responses by hand: weighted response = old response times weight, and use -reg- or -egen- to get the difference in the means of the weighted responses. Steve ) On Mon, Aug 2, 2010 at 10:11 AM, Stas Kolenikov <skolenik@gmail.com> wrote: > In what you describe below, the weights are not part of your data, but > rather are derived variables used as means to get the estimates (see > Steve's comments: aweights is not the right Stata concept to use here; > I completely agree with him). Hence, if you insist on the bootstrap, > an appropriate procedure that would replicate the analysis process on > the original sample would be: > > 1. take the bootstrap sample > 2. run your propensity/matching/covariate adjustment model > 3. compute the weights > 4. compute the treatment effect estimate(s) using these weights > 5. run 1-4 a large number of times. > > As always with the bootstrap, I won't buy this procedure until I see > the proof of consistency published in Biometrika or J of Econometrics. > If you are just manipulating the means and other moments of the data > in the re-weighting procedure, you are probably OK; if you are doing > matching, you are certainly not OK, as matching is not a smooth > operation. If you have a complex sampling procedure, you can probably > just forget about getting the standard errors right as even the first > step, getting a bootstrap sample that would resemble the complex > sample at hand, is far from trivial. (In sum: the bootstrap is a great > method when you are conducting inference for the mean; everything else > is complicated.) > > I would say that using the difference in weights that Steve suggested > is certainly an easier thing to do, although who knows how each > particular command will interpret the negative weights. It might also > be possible to get non-positive definite covariance matrix of the > coefficient estimates if weights are not all positive. > > Also, the more sensitivity analyses you run, the far off your overall > type I error is going to be. > > On Sun, Aug 1, 2010 at 12:39 PM, Ariel Linden, DrPH > <ariel.linden@gmail.com> wrote: >> There are at least two conceptual reasons why this process makes sense. >> >> First, assume a causal inference model which uses a weight (let's say an >> "average treatment on the treated" weight) to create balance on observed >> pre-intervention covariates (by setting the covariates to equal that of the >> treated group). Let's say the second model is identical but uses an "average >> treatment on controls" (ATC) weight. Assuming no unmeasured confounding, the >> treatment variable(s) from both models will provide the treatment effect >> estimate given the respective weighting purposes (holding covariates to >> represent treatment or control group characteristics). Thus, measuring the >> difference between the treatment effects in both models (which will need to >> have either bootstrapped or other adjustment to the SE) can serve as a >> sensitivity analysis (one of many approaches). >> >> Second, and in a similar manner, one can test the effect of a mediator using >> a weighting method for the original X-Y model, and second weight for the >> X-M-Y model. In both cases, different weights must be applied to two >> different regression models, and in both cases, the SE's will need to be >> adjusted. Weights are used in these models in a similar context to those in >> the first example - to control for confounding. >> >> By the way, a user written program called sgmediation (search sgmediation) >> does something similar to this but without the weights, so it may be >> possible to replicate many of the steps (or add weights?). > > -- > 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/

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

**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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