Lmtp: An R Package for Estimating the Causal Effects of Modified Treatment Policies

Abstract

We present the lmtp R package for causal inference from longitudinal observational or randomized studies. This package implements the estimators of Díaz et al. (2021) for estimating general non-parametric causal effects based on modified treatment policies. Modified treatment policies generalize static and dynamic interventions, making lmtp and all-purpose package for non-parametric causal inference in observational studies. The methods provided can be applied to both point-treatment and longitudinal settings, and can account for time-varying exposure, covariates, and right censoring thereby providing a very general tool for causal inference. Additionally, two of the provided estimators are based on flexible machine learning regression algorithms, and avoid bias due to parametric model misspecification while maintaining valid statistical inference.