Judea Pearl on IPW - a great find!

Judea Pearl's recent post on the intuition behind inverse probability weighting (IPW) is not likely to be one that I send out to non-statistical collaborators, but I did find it very useful for applied statisticians who want to understand current statistical thinking on the theoretical basis for IPW, and how that should guide selection of variables to include in a model for the probabilities in question.

For anyone who is working with observational studies, where selection to treatment is non-random; or with analysis of survey data, where response rates are a concern and non-response bias is possible, the technique of re-weighting the observed data to compensate for the observational design is worth a good look. Guidance on how to think about the models is much needed- and I believe that this post goes a long way towards providing that guidance! I hope to post an application note or two once I've made some headway on at least two projects where this will be useful.

Doubly-robust estimation of treatment effect

Marie Davidian presented work published in the 2004 Statistics in Medicine paper in Spring of 2007 at NCSU - here are the presentation slides: Double Robustness in Estimation of Causal Treatment Effects.

The doubly robust estimator of treatment effect uses inverse probability weighting to adjust for selection into treatment and further regression adjustment in modeling treatment effect. Davidian shows that as long as either the propensity model or the regression model is correct, the doubly-robust estimator is a consistent estimator of the true treatment effect.

A SAS macro that performs doubly robust estimation is available from the Harry Guess research community web site at UNC.

TE Love Propensity Score Resources

Thomas E. Love teaches a great short course on Propensity Score Methods and has made resources, including his bibliography, a spreadsheet for sensitivity analysis, and presentation slides, available here.