The Comparison of Methods for Individual Treatment Effect Detection
This work addresses the problem of selecting optimal methods for individual treatment effect estimation, which is crucial in fields like marketing and medicine, but it is incremental as it focuses on comparing existing methods on a specific dataset.
The paper compared machine learning methods for estimating individual treatment effects, finding that a combination of Logistic Regression with Difference Score and Uplift Random Forest achieved the best prediction correctness on the top 30% of test dataset observations.
Today, treatment effect estimation at the individual level is a vital problem in many areas of science and business. For example, in marketing, estimates of the treatment effect are used to select the most efficient promo-mechanics; in medicine, individual treatment effects are used to determine the optimal dose of medication for each patient and so on. At the same time, the question on choosing the best method, i.e., the method that ensures the smallest predictive error (for instance, RMSE) or the highest total (average) value of the effect, remains open. Accordingly, in this paper we compare the effectiveness of machine learning methods for estimation of individual treatment effects. The comparison is performed on the Criteo Uplift Modeling Dataset. In this paper we show that the combination of the Logistic Regression method and the Difference Score method as well as Uplift Random Forest method provide the best correctness of Individual Treatment Effect prediction on the top 30\% observations of the test dataset.