txshift: Efficient estimation of the causal effects of stochastic interventions in R

Abstract

The txshift R package aims to provide researchers in (bio)statistics, epidemiology, health policy, econometrics, and related disciplines with access to cutting-edge statistical methodology for evaluating the causal effects of continuous-valued exposures. txshift estimates the causal effects of modified treatment policies (or ‘feasible interventions’), which take into account the natural value of an exposure in assigning an intervention level. What’s more, the package provides independent corrections for estimating such effects under two-phase sampling (e.g., case-control) designs, allowing for the methodology to be readily applied in a diversity of real-world experimental and observational studies.

Type
Publication
In Journal of Open Source Software
Nima Hejazi
Assistant Professor of Biostatistics

My research broadly concerns the intersection of causal inference and machine learning, including developing statistical methodology tailored to modern experiments and observational studies in the biomedical and public health sciences.