lmw: Linear Model Weights for Causal Inference

TL;DR

This paper introduces the lmw R package that computes and evaluates linear model weights to assess how well regression adjustments in observational studies mimic randomized experiments, focusing on covariate balance and representativeness.

Contribution

The paper presents a novel software tool for empirically evaluating linear model weights in causal inference, applicable across various settings including instrumental variables and multi-valued treatments.

Findings

Provides diagnostics for linear model weights to assess covariate balance.

Applicable in diverse causal inference scenarios, including instrumental variables.

Enhances understanding of how regression adjustments approximate randomized experiments.

Abstract

The linear regression model is widely used in the biomedical and social sciences as well as in policy and business research to adjust for covariates and estimate the average effects of treatments. Behind every causal inference endeavor there is a hypothetical randomized experiment. However, in routine regression analyses in observational studies, it is unclear how well the adjustments made by regression approximate key features of randomized experiments, such as covariate balance, study representativeness, sample boundedness, and unweighted sampling. In this paper, we provide software to empirically address this question. We introduce the lmw package for R to compute the implied linear model weights and perform diagnostics for their evaluation. The weights are obtained as part of the design stage of the study; that is, without using outcome information. The implementation is general and applicable, for instance, in settings with instrumental variables and multi-valued treatments; in essence, in any situation where the linear model is the vehicle for adjustment and estimation of average treatment effects with discrete-valued interventions.