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
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