simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data

Journal of Statistical Software
Oleg SofryginRomain Neugebauer

Abstract

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 o...Continue Reading

Citations

Apr 24, 2018·Statistics in Medicine·Michael Schomaker, Christian Heumann
Jan 17, 2020·American Journal of Epidemiology·Jacqueline M TorresM Maria Glymour
Oct 1, 2019·Journal of the American Statistical Association·Jessica G YoungMiguel A Hernán
Feb 14, 2020·International Journal of Public Health·André MoserMarcel Zwahlen

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