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


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.

In Journal of Open Source Software
Nima Hejazi
Nima Hejazi
PhD Candidate in Biostatistics

My research interests lie at the intersection of causal inference and machine learning, especially as applied to the statistical analysis of complex data from observational studies and experiments in the biomedical and health sciences.