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

Author

Listed:
  • Yuya Sasaki

    (Vanderbilt University)

  • Takuya Ura

    (University of California, Davis)

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).

Suggested Citation

  • Yuya Sasaki & Takuya Ura, 2022. "Average treatment effect estimates robust to the “limited overlap” problem: robustate," Stata Journal, StataCorp LP, vol. 22(2), pages 344-354, June.
    • Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:344-354
    DOI: 10.1177/1536867X221106402
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