B0250
Title: Improved doubly robust inference for treatment effect heterogeneity using nonparametric and high-dimensional models
Authors: Joseph Antonelli - University of Florida (United States) [presenting]
Heejun Shin - University of Florida (United States)
Abstract: A doubly robust approach is proposed to characterizing treatment effect heterogeneity in observational studies. We utilize posterior distributions for both the propensity score and outcome regression models to provide valid inference on the conditional average treatment effect even when high dimensional or nonparametric models are used. We show that our approach provides conservative inference in finite samples or under model misspecification, and provides a consistent estimate of the variance of the causal effect when both models are correctly specified. In simulations we illustrate the utility of these results in difficult settings such as high dimensional covariate spaces or highly flexible models for the propensity score and outcome regression. Lastly, we analyze environmental exposure data from NHANES to identify how the effects of these exposures vary by subject level characteristics.
