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Doubly robust estimation of local average treatment effects with a binary instrument.

drlate estimates the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) from observational data with a binary instrument, a continuous/binary/count treatment, and a continuous/binary/count outcome. It implements the IPWRA, AIPW, IPW, and RA estimators of Słoczyński, Uysal, and Wooldridge (2022), with robust, cluster-robust, and bootstrap standard errors. It is an R port of the Stata drlate command and is validated against it to roughly 1e-8 on the SIPP example data.

Installation

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

Usage

library(drlate)

set.seed(1)
n <- 200
x <- rnorm(n)
z <- rbinom(n, 1, plogis(0.3 * x))                  # binary instrument
d <- rbinom(n, 1, plogis(-0.2 + 0.8 * z + 0.3 * x)) # treatment
y <- 1 + 0.5 * d + 0.4 * x + rnorm(n)               # outcome
dat <- data.frame(y, d, z, x)

# Default: doubly robust IPWRA LATE
drlate(y ~ x, d ~ x, z ~ x, data = dat)

# AIPW with inverse-probability-tilting PS; LATT; bootstrap SEs
drlate(y ~ x, d ~ x, z ~ x, data = dat, method = "aipw", ps = "ipt")
drlate(y ~ x, d ~ x, z ~ x, data = dat, estimand = "latt")
drlate(y ~ x, d ~ x, z ~ x, data = dat, vce = "bootstrap", boot_reps = 200)

The three formulas give, in order, the outcome model, the treatment model, and the instrument propensity-score model. Methods are print/summary/coef/ vcov/nobs/broom::tidy.

Documentation

Full documentation: https://xiangao.github.io/drlate/

Reference

Słoczyński, T., Uysal, S. D., and Wooldridge, J. M. (2022). Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment. arXiv:2208.01300. https://doi.org/10.48550/arXiv.2208.01300