Simcausal R package: Conducting transparent and reproducible simulation studies of causal effect estimation with complex longitudinal data

The simcausal r package is a tool for specification and simulation of complex longitudinal data structures that are based on non-parametric structural equation models. the package aims to provide a flexible tool for simplifying the conduct of transparent and reproducible simulation studies,with a particular emphasis on the types of data and interventions frequently encountered in real-world causal inference problems,such as,observational data with time-dependent confounding,selection bias,and random monitoring processes. the package interface allows for concise expression of complex functional dependencies between a large number of nodes,where each node may represent a measurement at a specific time point. the package allows for specification and simulation of counterfactual data under various user-specified interventions (e.g.,static,dynamic,deterministic,or stochastic). in particular,the interventions may represent exposures to treatment regimens,the occurrence or non-occurrence of right-censoring events,or of clinical monitoring events. finally,the package enables the computation of a selected set of user-specified features of the distribution of the counterfactual data that represent common causal quantities of interest,such as,treatment-specific means,the average treatment effects and coefficients from working marginal structural models. the applicability of simcausal is demonstrated by replicating the results of two published simulation studies. © 2017,american statistical association. all rights reserved.