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Treatment-effects estimation using lasso

Author

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  • Di Liu

    (StataCorp LLC)

Abstract

You can use treatment-effects estimators to draw causal inferences from observational data. You can use lasso when you want to control for many potential covariates. With standard treatment-effects models, there is an intrinsic con

Suggested Citation

  • Di Liu, 2024. "Treatment-effects estimation using lasso," Chinese Stata Conference 2024 09, Stata Users Group.
    • Handle: RePEc:boc:chin24:09
    as

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    File URL: http://repec.org/chin2024/China24_Liu.pdf
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    References listed on IDEAS

    as
    1. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(1), pages 21-59, February.
    2. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    3. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    4. Vira Semenova & Victor Chernozhukov, 2021. "Debiased machine learning of conditional average treatment effects and other causal functions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 264-289.
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