Re: st: IPW with OLOGIT

[Steven Samuels <sjhsamuels@earthlink.net>]

Correction: Instead of "individual predictors" in my last point but  
one, read "individual observations"

-S-
On Apr 7, 2008, at 1:31 PM, Steven Samuels wrote:


> Michael,
>
> I am not expert in this area. That said, your post is confusing to me.
>
> • I don't see why you want to use -ologit-.  The important part of  
> a treatment selection model is to model the probability of  
> selection into a treatment group.  The outcomes of treatments might  
> be ordered by treatment, but there is no reason to assume a priori  
> that the selection probabilities should be ordered by treatment  
> number.
>
> • You state you have a count outcome, but your specification for  
> the -glm- command is for a binary outcome.
>
> • The -glm- command does not take a -svy- prefix.  Try "help  
> svy_estimation" for a list of commands that will take survey  
> weights and design factors.
>
> •To use a survey weight, you would need to -svyset- your data after  
> creating your new weights; then you would need to use the -svy-  
> prefix for your command.
>
> • Wooldridge's example of treatment selection is for the purpose of  
> estimating individual population means; then taking the difference  
> between those means. For that purpose you would need to define  
> three weights ipw1, ipw2, ipw3 based on p1, p2, and p3. (See help  
> for -generate-) Run your second regression model three times, each  
> with a different weight; then average the predicted values over the  
> entire sample. The post-estimation command -predictnl- might  
> compute the difference between means.
>
> • Wooldridge's method would estimate a difference between   
> populations means unadjusted for covariates.  Is this what you want?
>
> • Apparently Wooldridge's doubly-robust variance-matrices take into  
> account variability due to computing the propensity scores,  
> although I don't quite follow the argument .  You could also  
> bootstrap or jackknife the entire process.  As sums are over the  
> entire sample, then an estimated contrast in means is an average of  
> the contrast in the predicted values of individual predictions.
>
>
> • As you have the same variables ("$var") on the right hand side of  
> your treatment and outcome equations, I don't see a need for the  
> IPW model at all.
>
>
> -Steven
>
>
> > I am trying to adjust for selection on observables using propensity
> > scores as inverse probability weights (ipw), following Wooldridge  
> > ("IPW
> > Estimation for General Missing Data Problems").  My dataset has a
> > complex survey design with survey weights (svywt), and I want to  
> > adjust
> > for selection bias of an ordered treatment (t1,t2,t3) on a count
> > outcome.  Can someone help me with the Stata code to compute the IPW
> > using the predicted probabilities as propensity scores?  I want to
> > compute IPW by multiplying the survey weight*(1/propensity score),
> > following Zanutto et al. (2005).
> >
> > ologit t_cat $var
> > predict p1 p2 p3
> >
> > /*Here's where I need help*/
> > ipw=svywt*[1/p1 ...]
> >
> > glm depvar $var t2 t3 [pweight=ipw], fam(bin) link(logit) irls robust
> >
> >
> > Thanks in advance,
> > Mike
> >
> >
> > Michael F. Furukawa, PhD
> > Assistant Professor
> > Health Management and Policy
> > W. P. Carey School of Business
> > Arizona State University
> > (480) 965-2363
> >
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/support/faqs/res/findit.html
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
>
> *
> *   For searches and help try:
> *   http://www.stata.com/support/faqs/res/findit.html
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/