The Comparison of Methods for IndividualTreatment Effect Detection

Daria Semenova and Maria Temirkaeva

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Individual Treatment Effect; ITE; Machine Learning; Random Forest; XGBoost; SVM·Random; Experiments; A/B testing; Uplift Random Forest (search for similar items in EconPapers)
JEL-codes: C10 M30 (search for similar items in EconPapers)
Date: 2019-09-23, Revised 2019-09-23
New Economics Papers: this item is included in nep-big, nep-ecm and nep-ore
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