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This paper constructs a doubly robust estimator for continuous dose-responseestimation. An outcome regression model is augmented with a set of inversegeneralized propensity score covariates to correct for potentialmisspecification bias. From the augmented model we can obtain consistentestimates of mean average potential outcomes for distinct strata of thetreatment. A polynomial regression is then fitted to these point estimates toderive a Taylor approximation to the continuous dose-response function. Thebootstrap is used for variance estimation. Analytical results and simulationsshow that our approach can provide a good approximation to linear or nonlineardose-response functions under various sources of misspecification of theoutcome regression or propensity score models. Efficiency in finite samples isgood relative to minimum variance consistent estimators.

Doubly robust dose-response estimation for continuous treatments via generalized propensity score augmented outcome regression