Uplift Random Forests

Abstract

Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units. This is the case, for example, in treatment=control settings, such as those usually encountered in marketing and clinical trial applications. In these situations, we may not necessarily be interested in predicting the outcome itself, but in estimating the expected change in the outcome as a result of the action. This is precisely the idea behind uplift models, which, despite their many practical applications, have received little attention in the literature. In this article, we extend the state-of-theart research in this area by proposing a new approach based on Random Forests. We perform carefully designed experiments using simple simulation models to illustrate some of the properties of the proposed method. In addition, we present evidence on a dataset pertaining to a large Canadian insurer on a customer retention case. The results confirm the effectiveness of the proposed method and show favorable performance relative to other existing uplift modeling approaches.

Response transformation and profit decomposition for revenue uplift modeling

“…Given that none of the tree-based techniques has been employed for modeling revenue uplift in e-commerce, the evaluation also broadens the scope of empirical results for causal machine learning methods. Causal Forest Cai et al (2011) Two-Step Estimation Procedure Chickering and Heckerman (2000) Uplift Tree with Post-Processing Procedure Diemert et al (2018) x - Guelman et al (2015a) Causal Conditional Inference Tree/Forest Guelman et al (2015b) Uplift Random Forests Gutierrez and Gérardy (2017) -x Hahn et al (2019) Causal Bayesian Regression Trees Hansen and Bowers (2008) x - Hansotia and Rukstales (2002a) Incremental Response Tree Hansotia and Rukstales (2002b) Uplift Tree with the ∆∆ splitting criterion Causal BART Imai and Ratkovic (2013) Uplift Support Vector Machine Jaroszewicz and Rzepakowski (2014) Uplift k-Nearest Neighbors Kane et al (2014) x - Kuusisto et al (2014) Uplift Support Vector Machine Künzel et al (2019) X-Learner Lai et al (2006) x - Lechner (2019) Modified Causal Forests Lo (2002) x - Lo and Pachamanova (2015) Multiple Treatments Logistic Regression Nassif et al (2013) x Oprescu et al (2018) Orthogonal Causal Random Forest Powers et al (2018) Causal boosting Radcliffe and Surry (1999) Uplift Trees Radcliffe and Surry (2011) -x Rzepakowski and Jaroszewicz (2012a) Multiple Treatments Uplift Trees Rzepakowski and Jaroszewicz (2012b) Information Theory-Based Uplift Trees Rudaś and Jaroszewicz (2018) x - Shaar et al (2016) Pessimistic Uplift Shalit et al (2017) Causal Artificial Neural Network Sołtys et al (2015) Uplift Ensemble Methods Su et al (2012) Uplift k-Nearest Neighbors Taddy et al (2016) Causal Bayesian Forests Tian et al (2014) x - Yamane et al (2018) Separate Label Uplift Modeling This study…”

Predicting Probability of Customer Churn in Insurance

“…So, finding the reasons of customer churn and identifying factors that explain the switching behaviour [8] is a key aspect for the correct management and strategy of an insurance firm. The need for implementing short-term actions oriented to improve customer satisfaction and reverse the intention to leave is the reason for modelling and predicting the probability of churn through predictive analytical models [9][10][11][12][13].…”

Uplift Modeling for Multiple Treatments with Cost Optimization

“…The focus of this paper is to discuss and evaluate uplift modeling approaches for two common practical challenges: (1) when there are multiple treatment groups present and (2) when there are different costs associated with the treatments. The existing literature on uplift modeling has largely focused on the situation where there is just one treatment and one control.…”