Package: drlate 0.3.1

drlate: 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) <doi:10.48550/arXiv.2208.01300> -- 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) <doi:10.1080/07350015.2024.2332763>. 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).

Authors:Kailas Venkitasubramanian [aut, cre], S. Derya Uysal [ctb, cph], Tymon Sloczynski [ctb, cph], Jeffrey M. Wooldridge [ctb, cph]

drlate_0.3.1.tar.gz
drlate_0.3.1.zip(r-4.7)drlate_0.3.1.zip(r-4.6)drlate_0.3.1.zip(r-4.5)
drlate_0.3.1.tgz(r-4.6-any)drlate_0.3.1.tgz(r-4.5-any)
drlate_0.3.1.tar.gz(r-4.7-any)drlate_0.3.1.tar.gz(r-4.6-any)
drlate_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
drlate/json (API)

# Install 'drlate' in R:
install.packages('drlate', repos = c('https://kvenkita.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kvenkita/drlate/issues

Pkgdown/docs site:https://kvenkita.github.io

Datasets:

On CRAN:

Conda:

4.48 score 1 stars 7 exports 1 dependencies

Last updated from:fb6475e4f8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK190
source / vignettesOK199
linux-release-x86_64OK150
macos-release-arm64OK139
macos-oldrel-arm64OK154
windows-develOK123
windows-releaseOK124
windows-oldrelOK133
wasm-releaseOK106

Exports:balancebalance_testcomplier_meansdr_hausmandrlatedrlate_comparekappa_weights

Dependencies:numDeriv

A primer: doubly robust LATE estimation, from intuition to practice
1. The problem: a treatment people choose | Why covariates enter | 2. The four core estimators in one picture | Why the paper (and the package default) prefers IPWRA | 3. A worked example | The naive answers fail | The drlate answer | Seeing double robustness work | 4. Checking the design: diagnostics | Overlap | Covariate balance | Weight distributions | A formal balance test | Profiling the compliers | 5. Choosing models and options | Outcome and treatment families | Instrument propensity score flavors | LATT: the effect for treated compliers | 6. Abadie's kappa: the weighting-estimator menu | 7. When the instrument is weak: Fieller confidence sets | 8. Bootstrap inference | 9. How much does the estimator choice matter? | 10. Do you even need the instrument? The DR Hausman test | 11. Coming from Stata | References

Last update: 2026-06-15
Started: 2026-06-04

Doubly robust estimation of the LATE and LATT with drlate
Overview | Joint inference | Example | Other estimators | Abadie-kappa weighting estimators | LATT, other model families, and IPT | Clustered standard errors and weights | Diagnostics | Inference beyond the default sandwich | The DR Hausman test of unconfoundedness | Comparing estimators | Replicating the Stata examples | Citation | References

Last update: 2026-06-15
Started: 2026-06-04