Doubly Robust Estimation of Local Average Treatment Effects

Estimates the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) using observational data with a binary instrument, implementing the complete estimator suite of Sloczynski, Uysal, and Wooldridge: the doubly robust estimators of Sloczynski, Uysal, and Wooldridge (2022) -- inverse probability weighted regression adjustment (IPWRA), inverse probability weighting (IPW), augmented inverse probability weighting (AIPW), and regression adjustment (RA) -- and the Abadie-kappa weighting estimators of Sloczynski, Uysal, and Wooldridge (2025) . Supports linear, logistic, probit, Poisson, and fractional (fractional-logit and fractional-probit) outcome and treatment models, and instrument propensity scores estimated by maximum likelihood, covariate balancing (CBPS), or inverse probability tilting (IPT). Standard errors are computed jointly for all estimation stages by stacking the moment conditions of every model into a single M-estimation system; weak-instrument-robust Fieller confidence sets, cluster-aware bootstrap inference, design diagnostics, and a doubly robust Hausman-type test of unconfoundedness are included. Estimates and standard errors are validated against the authors' Stata commands 'drlate' (Statistical Software Components S459708) and 'kappalate' (S459257).

drlate

drlate provides doubly robust estimation of the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) from observational data with a binary instrument. It implements the complete estimator program of Słoczyński, Uysal, and Wooldridge in one package: the doubly robust IPWRA/IPW/AIPW/RA estimators of Słoczyński, Uysal & Wooldridge (2022) and the Abadie-kappa weighting estimators of Słoczyński, Uysal & Wooldridge (2025, JBES), behind a single three-formula interface. Standard errors for every estimator come from one jointly stacked M-estimation system whose sandwich variance accounts for all estimation stages, and the test suite verifies numerical equivalence of estimates and standard errors against fixtures generated by the authors' own Stata commands (drlate, SSC S459708, and kappalate, SSC S459257) across 38 scenarios. Around the estimators the package provides the workflow applied IV analysis needs: design diagnostics, weak-instrument-robust Fieller confidence sets, cluster-aware bootstrap inference, estimator comparison, and the 2022 paper's doubly robust Hausman test of unconfoundedness.

Installation

# install.packages("remotes")
remotes::install_github("kvenkita/drlate")

Example

library(drlate)
data(drlate_sim)

fit <- drlate(lwage ~ age + educ,    # outcome model
              nvstat ~ age + educ,   # treatment model
              rsncode ~ age + educ,  # instrument propensity score model
              data = drlate_sim)
summary(fit)
#> Local average treatment effect
#> Number of obs    : 2,000
#> Estimator        : IPWRA
#> Outcome model    : linear
#> Treatment model  : logit
#> Instrument model : logit (MLE)
#>
#>              Estimate Std. Error z value   Pr(>|z|) [95% conf. interval]
#> LATE: D on Y   0.4705    0.07915   5.944  2.786e-09     0.3153    0.6256
#> ATE: Z on Y    0.2845    0.05043   5.642  1.679e-08     0.1857    0.3834
#> ATE: Z on D    0.6048    0.01837  32.929 8.326e-238     0.5688    0.6408
#>
#> First stage (Z on D): z = 32.93 (z^2 ~ first-stage F = 1084)

Citation

If you use drlate in your research, please cite:

Venkitasubramanian, K. (2026). drlate: Doubly Robust Estimation of the Local Average Treatment Effect in R. R package version 0.3.0. https://github.com/kvenkita/drlate

and the methodological papers whose estimators you use:

Słoczyński, T., Uysal, S. D., & Wooldridge, J. M. (2022). Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment. arXiv:2208.01300.

Słoczyński, T., Uysal, S. D., & Wooldridge, J. M. (2025). Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect. Journal of Business & Economic Statistics 43(1), 164–177.

The package descends from the authors' Stata commands:

Uysal, D., Słoczyński, T., & Wooldridge, J. M. (2026). DRLATE: Stata module to perform doubly robust estimation of the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT). Statistical Software Components S459708, Boston College Department of Economics.

(citation("drlate") prints the entries with BibTeX.)

Features

Documentation

The package website serves the primer, the package overview and Stata replication, and the function reference.

License

MIT. Portions derived from the Stata package drlate, © 2026 S. Derya Uysal, Tymon Słoczyński, and Jeffrey M. Wooldridge (MIT licensed).