  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.
