Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

This article is an excellent introduction to doubly robust methods and we congratulate the authors for their thoroughness in bringing together the wide array of methods from different traditions that all share the property of being doubly robust.Statisticians at RAND have been making extensive use of propensity score weighting in education (McCaffrey and Hamilton, 2007), policing and criminal justice (Ridgeway, 2006), drug treatment evaluation (Morral et al., 2006), and military workforce issues (Harrell, Lim, Castaneda and Golinelli, 2004).More recently, we have been adopting doubly robust (DR) methods in these applications believing that we could achieve further bias and variance reduction.Initially, this article made us second-guess our decision.The apparently strong performance of OLS and the authors' finding that no method outperformed OLS ran counter to our intuition and experience with propensity score weighting and DR estimators.We posited two potential explanations for this.First, we suspected that the high variance reported by the authors when using propensity score weights could result from their use of standard logistic regression.Second, stronger interaction effects in the outcome regression model might favor the DR approach. METHODSWe felt the authors were somewhat narrow in their discussion of weighting by focusing only on propensity scores estimated by logistic regression in their simulation.The high variability in the weights reported by