An alternative robust estimator of average treatment effect in causal inference.

ABSTRACT: The problem of estimating the average treatment effects is important when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating the average treatment effect rely on some parametric assumptions about the propensity score model or the outcome regression model one way or the other. In reality, both models are prone to misspecification, which can have undue influence on the estimated average treatment effect. We propose an alternative robust approach to estimating the average treatment effect based on observational data in the challenging situation when neither a plausible parametric outcome model nor a reliable parametric propensity score model is available. Our estimator can be considered as a robust extension of the p

SUBMITTER: Liu J 

PROVIDER: S-EPMC6089681 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature