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The Comparison of Methods for IndividualTreatment Effect Detection

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  • 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

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    File URL: https://mpra.ub.uni-muenchen.de/97309/1/paper4.pdf
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    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.
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    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

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