IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/97309.html
   My bibliography  Save this paper

The Comparison of Methods for IndividualTreatment Effect Detection

Author

Listed:
  • Semenova, Daria
  • Temirkaeva, Maria

Abstract

Today, treatment effect estimation at the individual level isa vital problem in many areas of science and business. For example, inmarketing, estimates of the treatment effect are used to select the mostefficient promo-mechanics; in medicine, individual treatment effects areused to determine the optimal dose of medication for each patient and soon. At the same time, the question on choosing the best method, i.e., themethod that ensures the smallest predictive error (for instance, RMSE)or the highest total (average) value of the effect, remains open. Accord-ingly, in this paper we compare the effectiveness of machine learningmethods for estimation of individual treatment effects. The comparisonis performed on the Criteo Uplift Modeling Dataset. In this paper weshow that the combination of the Logistic Regression method and theDifference Score method as well as Uplift Random Forest method pro-vide the best correctness of Individual Treatment Effect prediction onthe top 30% observations of the test dataset.

Suggested Citation

  • Semenova, Daria & Temirkaeva, Maria, 2019. "The Comparison of Methods for IndividualTreatment Effect Detection," MPRA Paper 97309, University Library of Munich, Germany, revised 23 Sep 2019.
    • Handle: RePEc:pra:mprapa:97309
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/97309/1/paper4.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu Tian & Ash A. Alizadeh & Andrew J. Gentles & Robert Tibshirani, 2014. "A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1517-1532, December.
    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. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    3. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    4. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    5. 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.
    6. Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
    7. Peng Wu & Donglin Zeng & Haoda Fu & Yuanjia Wang, 2020. "On using electronic health records to improve optimal treatment rules in randomized trials," Biometrics, The International Biometric Society, vol. 76(4), pages 1075-1086, December.
    8. Buhl-Wiggers, Julie & Kerwin, Jason & Muñoz-Morales, Juan S. & Smith, Jeffrey A. & Thornton, Rebecca L., 2020. "Some Children Left Behind: Variation in the Effects of an Educational Intervention," IZA Discussion Papers 13598, Institute of Labor Economics (IZA).
    9. Weibin Mo & Yufeng Liu, 2022. "Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 440-472, April.
    10. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    11. Adam Ciarleglio & Eva Petkova & Todd Ogden & Thaddeus Tarpey, 2018. "Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1331-1356, November.
    12. Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
    13. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    14. Muxuan Liang & Menggang Yu, 2023. "Relative contrast estimation and inference for treatment recommendation," Biometrics, The International Biometric Society, vol. 79(4), pages 2920-2932, December.
    15. Baqun Zhang & Min Zhang, 2018. "C‐learning: A new classification framework to estimate optimal dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 74(3), pages 891-899, September.
    16. 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.
    17. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
    18. Susan Athey & Guido Imbens, 2015. "Recursive Partitioning for Heterogeneous Causal Effects," Papers 1504.01132, arXiv.org, revised Dec 2015.
    19. Zhang, Yuyang & Schnell, Patrick & Song, Chi & Huang, Bin & Lu, Bo, 2021. "Subgroup causal effect identification and estimation via matching tree," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    20. Guilloux, Agathe & Lemler, Sarah & Taupin, Marie-Luce, 2016. "Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 141-159.

    More about this item

    Keywords

    Individual Treatment Effect; ITE; Machine Learning; Random Forest; XGBoost; SVM·Random; Experiments; A/B testing; Uplift Random Forest;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:97309. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.