Uplift modeling is an area of machine learning which aims at predicting the causal effect of some action on a given individual. The action may be a medical procedure, marketing campaign, or any other circumstance controlled by the experimenter. Building an uplift model requires two training sets: the treatment group, where individuals have been subject to the action, and the control group, where no action has been performed. An uplift model allows then to assess the gain resulting from taking the action on a given individual, such as the increase in probability of patient recovery or of a product being purchased. This paper describes an adaptation of the well-known boosting techniques to the uplift modeling case. We formulate three desirable properties which an uplift boosting algorithm should have. Since all three properties cannot be satisfied simultaneously, we propose three uplift boosting algorithms, each satisfying two of them. Experiments demonstrate the usefulness of the proposed methods, which often dramatically improve performance of the base models and are thus new and powerful tools for uplift modeling.
Some work has also been published on using ensemble methods for uplift modeling, although, to the best of our knowledge, none of them on boosting. Bagging of uplift models has been mentioned in @cite_10 . Uplift Random Forests have been proposed by @cite_6 ; an extension, called causal conditional inference trees was proposed by the same authors in @cite_0 . A thorough experimental and theoretical analysis of bagging and random forests in uplift modeling can be found in @cite_17 where it is argued that ensemble methods are especially well suited to this task and that bagging performs surprisingly well.
@cite_0 @cite_10 @cite_6 @cite_17
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We consider a model for price calculations based on three components: a fair premium; price loadings reflecting general expenses and solvency requirements; and profit. The first two components are typically evaluated on a yearly basis, while the third is viewed from a longer perspective. When considering the value of customers over a period of several years, and examining policy renewals and cross-selling in relation to price adjustments, many insurers may prefer to reduce their short-term benefits so as to focus on their most profitable customers and the long-term value. We show how models of personalized treatment learning can be used to select the policy holders that should be targeted in a company’s marketing strategies. An empirical application of the causal conditional inference tree method illustrates how best to implement a personalized cross-sell marketing campaign in this framework. Models of customer churn are based on historical data and are used to predict the probability that a client switches to another company. We address customer retention in insurance. Rather than concentrating on those customers with high probability of leaving, we propose a new procedure that can be used to identify the target customers who are likely to respond positively to a retention activity. Our approach is based on random forests and can be useful to anticipate the success of marketing actions aimed at reducing customer attrition. We also discuss the type of insurance portfolio database that can be used for this purpose. Uplift modeling is a branch of machine learning which aims at predicting the causal effect of an action such as a marketing campaign or a medical treatment on a given individual by taking into account responses in a treatment group, containing individuals subject to the action, and a control group serving as a background. The resulting model can then be used to select individuals for whom the action will be most profitable. This paper analyzes the use of ensemble methods: bagging and random forests in uplift modeling. We perform an extensive experimental evaluation to demonstrate that the application of those methods often results in spectacular gains in model performance, turning almost useless single models into highly capable uplift ensembles. The gains are much larger than those achieved in case of standard classification. We show that those gains are a result of high ensemble diversity, which in turn is a result of the differences between class probabilities in the treatment and control groups being harder to model than the class probabilities themselves. The feature of uplift modeling which makes it difficult thus also makes it amenable to the application of ensemble methods. As a result, bagging and random forests emerge from our evaluation as key tools in the uplift modeling toolbox.