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.

Sample of titles from Gary King's works in progress

I was just looking up one of Gary King's papers on his preprint page, since I don't have access to the journal in which it was published. I found so many interesting and useful preprints there that I had a hard time just getting to what I was looking for. A sampling of the titles is the quickest way to indicate the range and relevance of his works:

- Measuring Total Health Inequality: Adding Individual Variation to Group-Level Differences

- The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation

- The Future of Death in America

- How Not to Lie Without Statistics

- Matching for Causal Inference Without Balance Checking

- A `Politically Robust' Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program

- What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation

- Armed Conflict as a Public Health Problem

This list also provides inspiration towards better titles in my future publications. Gary has a paper with advice on writing for publication, too - see his site.