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Statistical causal inference has traditionally focused on effects defined by inflexible static interventions, applicable only to binary or categorical exposures. The evaluation of such interventions is often plagued by many problems, both theoretical (e.g., non-identification) and practical (e.g., positivity violations); however, stochastic interventions provide a promising solution to these fundamental issues (Dı́az & van der Laan, 2018). The txshift R package provides researchers in (bio)statistics, epidemiology, health policy, economics, and related disciplines with access to state-of-the-art statistical methodology for evaluating the causal effects of stochastic shift interventions on 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. To accommodate use in study designs incorporating outcome-dependent two-phase sampling (e.g., case-control), the package provides two types of modern corrections, both rooted in semiparametric theory, for constructing unbiased and efficient estimates, despite the significant limitations induced by such designs. Thus, txshift makes possible the estimation of the causal effects of stochastic interventions in experimental and observational study settings subject to real-world design limitations that commonly arise in modern scientific practice.

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