ROBUSTATE: Stata module for estimation and inference for the average treatment effect (ATE) robustly against the limited overlap

Yuya Sasaki and Takuya Ura
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Takuya Ura: University of California, Davis

Statistical Software Components from Boston College Department of Economics

Abstract: robustate executes estimation and inference for the average treatment effect (ATE) robustly against the limited overlap based on Sasaki and Ura (2021) -- Section 7. Under the limited overlap, the naive inverse propensity score weighting estimation method suffers from large variances (if not a lack of the consistency or the asymptotic normality). Hence, it is a common practice to trim observations whose propensity scores are close to 0 or 1, but such a practice biases the estimator of the ATE. This command, robustate, corrects the bias from the trimming and computes a valid standard error accounting for the bias correction. The command accepts an outcome variable, a binary treatment variable, and a list of control variables. It returns both the naive inverse propensity score weighted estimate and the robust inverse propensity score weighted estimate. The robust estimator in general yields a smaller standard error than the naive estimator.

Language: Stata
Requires: Stata version 14.2
Keywords: ATE; estimation; overlap; propensity score (search for similar items in EconPapers)
Date: 2021-04-06, Revised 2022-04-16
Note: This module should be installed from within Stata by typing "ssc install robustate". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/r/robustate.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/r/robustate.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/c/catheterization_small.dta sample data file (application/x-stata)

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