Average treatment effect estimates robust to the “limited overlap” problem: robustate

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

Stata Journal, 2022, vol. 22, issue 2, 344-354

Abstract: We introduce a new command, robustate, that executes the inverse-probability weighting estimation and inference for the average treatment effect with robustness against limited overlap (that is, weak satisfaction of the common support condition). This command produces estimates, standard errors, p-values, and confidence intervals for the average treatment effect. The utility of the com- mand is demonstrated with both simulated and real data of right heart catheteri- zation. These illustrations show that the proposed estimator implemented by the robustate command indeed exhibits more robustness against limited overlap than the traditional inverse-probability weighting estimator. The main method of the command is proposed in Sasaki and Ura (2022, Econometric Theory 38: 66–112).

Keywords: robustate; average treatment effect; bias correction; common support; inverse-probability weighting; limited overlap; robustness; trimming (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1177/1536867X221106402

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