IDEAS home Printed from https://ideas.repec.org/p/boc/isug22/07.html
   My bibliography  Save this paper

Treatment-effects estimation using lasso

Author

Listed:
  • Di Liu

    (StataCorp)

Abstract

One 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, 2022. "Treatment-effects estimation using lasso," Italian Stata Users' Group Meetings 2022 07, Stata Users Group.
    • Handle: RePEc:boc:isug22:07
    as

    Download full text from publisher

    File URL: http://repec.org/isug2022/Italy22_Liu.pdf
    File Function: presentation materials
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. 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.
    3. Poterba, James M. & Venti, Steven F. & Wise, David A., 1995. "Do 401(k) contributions crowd out other personal saving?," Journal of Public Economics, Elsevier, vol. 58(1), pages 1-32, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Di Liu, 2022. "Treatment-effects estimation using lasso," 2022 Stata Conference 07, Stata Users Group.
    2. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    3. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    4. Dingke Tang & Dehan Kong & Wenliang Pan & Linbo Wang, 2023. "Ultra‐high dimensional variable selection for doubly robust causal inference," Biometrics, The International Biometric Society, vol. 79(2), pages 903-914, June.
    5. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    6. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
    7. Oliver Dukes & Vahe Avagyan & Stijn Vansteelandt, 2020. "Doubly robust tests of exposure effects under high‐dimensional confounding," Biometrics, The International Biometric Society, vol. 76(4), pages 1190-1200, December.
    8. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    9. Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann & Achim Ahrens, 2022. "ddml: Double/debiased machine learning in Stata," Swiss Stata Conference 2022 02, Stata Users Group.
    10. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "Model Averaging and Double Machine Learning," Papers 2401.01645, arXiv.org.
    11. Kuppelwieser, Thomas & Wozabal, David, 2021. "Liquidity costs on intraday power markets: Continuous trading versus auctions," Energy Policy, Elsevier, vol. 154(C).
    12. Sida Peng, 2019. "Heterogeneous Endogenous Effects in Networks," Papers 1908.00663, arXiv.org.
    13. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    14. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    15. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    16. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    17. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    18. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    19. Ruoyao Shi, 2021. "An Averaging Estimator for Two Step M Estimation in Semiparametric Models," Working Papers 202105, University of California at Riverside, Department of Economics.
    20. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Nov 2023.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:isug22:07. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.