Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero.

doi:10.1093/biomet/asp033

PMCID: PMC2798744
PMID: 20161511

Causal inference; Enhanced propensity score model; Missing at random; No unmeasured con-founders; Outcome regression

Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero.

doi:10.1093/biomet/asp033

PMCID: PMC2798744
PMID: 20161511

Causal inference; Enhanced propensity score model; Missing at random; No unmeasured confounders; Outcome regression