Efficient nonparametric estimation of heterogeneous treatment effects in causal inference

PROJECT SUMMARY In nearly all studies of comparative effectiveness, the investigators seek to estimate how an in- tervention changes outcomes on average. That is, are outcomes for treated subjects better on average than for untreated subjects? While the average treatment effect (ATE) is a useful sum- mary of the treatment effect, the treatment effect may vary from patient to patient. The ATE is a low-dimensional summary of a treatment effect, since it summarizes the overall effect of the treatment using a single quantitative measure and ignores possible effect heterogeneity. Many in- vestigators seek to go beyond low-dimensional summaries by estimating heterogenous treatment effects (HTEs). The most common approach to the estimation of HTEs relies on simple statistical methods. Specifically, regression models are widely used but may be biased due to the linear functional form especially where HTEs that are nonlinear or based on complex combinations of patient subgroups. Currently, there is considerable interest in developing more flexible methods for the estimation of the HTEs. In this project, we will use the the doubly robust machine learning (DRML) framework to develop improved methods for a variety of HTEs. The DRML framework is a combination of semiparametric theory, machine learning (ML) methods, and doubly robust estimators. The key advantage of the DRML framework is that it allows one to reduce bias using ML estimation methods, while retaining the efficiency of parametric models.