An R package for parametric estimation of causal effects

TL;DR

The paper introduces the R package CausalModels, which provides a unified, accessible framework for various structural causal inference methods, addressing a software gap in observational data analysis.

Contribution

It offers a comprehensive R package that consolidates multiple causal inference methods into a single, user-friendly framework, enhancing practical application and consistency.

Findings

Includes standardization, IP weighting, G-estimation, outcome regression, instrumental variables, and propensity matching.

Facilitates causal effect estimation without extensive statistical expertise.

Addresses software gaps in existing causal inference tools.

Abstract

This article explains the usage of R package CausalModels, which is publicly available on the Comprehensive R Archive Network. While packages are available for sufficiently estimating causal effects, there lacks a package that provides a collection of structural models using the conventional statistical approach developed by Hernan and Robins (2020). CausalModels addresses this deficiency of software in R concerning causal inference by offering tools for methods that account for biases in observational data without requiring extensive statistical knowledge. These methods should not be ignored and may be more appropriate or efficient in solving particular problems. While implementations of these statistical models are distributed among a number of causal packages, CausalModels introduces a simple and accessible framework for a consistent modeling pipeline among a variety of statistical methods for estimating causal effects in a single R package. It consists of common methods including standardization, IP weighting, G-estimation, outcome regression, instrumental variables and propensity matching.