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Showing content from https://github.com/kolesarm/multe below:

kolesarm/multe: Multiple Treatment Effects Regression

This R package implements contamination bias diagnostics, using procedures from Goldsmith-Pinkham, Hull, and Kolesár (2024). See multe-stata for a Stata version of this package.

See vignette multe for description of the package (available through vignette("multe") once package is installed), and the package manual for documentation of the package functions.

This software package is based upon work supported by the National Science Foundation under grant numbers SES-22049356 (Kolesár), and by work supported by the Alfred P. Sloan Research Fellowship (Kolesár).

You can install the released version of multe from CRAN with:

install.packages("multe")

Alternatively, you can get the current development version from GitHub:

if (!requireNamespace("remotes")) {
  install.packages("remotes")
}
remotes::install_github("kolesarm/multe")

The packages takes the output of lm, and computes alternative estimates of the treatment effects that are free of contamination bias.

library("multe")
## Regression of IQ at 24 months on race indicators and baseline controls
r1 <- stats::lm(std_iq_24~race+factor(age_24)+female+SES_quintile, weight=W2C0, data=fl)
## Compute alternatives estimates free of contamination bias
m1 <- multe(r1, "race", cluster=NULL)
print(m1, digits=3)

This returns the following table:

PL OWN ATE EW CW Black -0.2574 -0.2482 -0.2655 -0.2550 -0.2604 SE 0.0281 0.0291 0.0298 0.0289 0.0292 Hispanic -0.2931 -0.2829 -0.2992 -0.2862 -0.2944 SE 0.0260 0.0267 0.0299 0.0268 0.0279 Asian -0.2621 -0.2609 -0.2599 -0.2611 -0.2694 SE 0.0343 0.0343 0.0418 0.0343 0.0475 Other -0.1563 -0.1448 -0.1503 -0.1447 -0.1522 SE 0.0369 0.0370 0.0359 0.0368 0.0370

In particular, the package computes the following estimates

PL :: The original estimate based on a partly linear model where covariates enter additively
OWN :: The own treatment effect component of the PL estimator that subtracts an estimate of the contamination bias
ATE :: The unweighted average treatment effect, implemented using regression that includes interactions of covariates with the treatment indicators
EW :: Weighted ATE estimator based on easiest-to-estimate weighting (EW) scheme, implemented by running one-treatment-at-a-time regressions.
CW :: Weighted ATE estimator using easiest-to-estimate common weighting (CW) scheme, implemented using weighted regression.

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