att_dr: Doubly robust DDD estimator for ATT, with panel data and 2...

In triplediff: Triple-Difference Estimators

Doubly robust DDD estimator for ATT, with panel data and 2 periods

Description

This function implements a doubly robust estimator for assessing the average treatment effect on the treated (ATT) using a triple differences (DDD) approach in panel data settings across two time periods. The function takes preprocessed data structured specifically for this analysis.

Usage

att_dr(did_preprocessed)

Arguments

did_preprocessed

A list containing preprocessed data and specifications for the DDD estimation. Expected elements include: - preprocessed_data: A data table containing the data with variables needed for the analysis. - est_method: The estimation method to be used. Default is est_method = "dr". - xformula: The formula for the covariates to be included in the model. It should be of the form ~ x1 + x2. Default is xformla = ~1 (no covariates). - boot: Logical. If TRUE, the function use the multiplier bootstrap to compute standard errors. Default is FALSE. - nboot: The number of bootstrap samples to be used. Default is NULL. If boot = TRUE, the default is nboot = 999. - subgroup_counts: A matrix containing the number of observations in each subgroup. - alpha The level of significance for the confidence intervals. Default is 0.05. - inffunc: Logical. If TRUE, the function returns the influence function. Default is FALSE. - use_parallel: Boolean of whether or not to use parallel processing in the multiplier bootstrap, default is use_parallel=FALSE - cores: the number of cores to use with parallel processing, default is cores=1 - cband: Boolean of whether or not to compute simultaneous confidence bands, default is cband=FALSE

Value

A list with the estimated ATT, standard error, upper and lower confidence intervals, and influence function.

triplediff documentation built on Aug. 8, 2025, 7:33 p.m.