A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling

Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium, Orange Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets.

Uplift vs. predictive modeling: a theoretical analysis

Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature. These strategies are crucial for practitioners in various domains, such as marketing, telecommunications, health care and finance. This paper presents a comprehensive treatment of the subject, starting from firm theoretical foundations and highlighting the parameters that influence the performance of the uplift and predictive approaches. The focus of the paper is on a binary outcome case and a binary action, and the paper presents a theoretical analysis of uplift modeling, comparing it with the classical predictive approach. The main research contributions of the paper include a new formulation of the measure of profit, a formal proof of the convergence of the uplift curve to the measure of profit ,and an illustration, through simulations, of the conditions under which predictive approaches still outperform uplift modeling. We show that the mutual information between the features and the outcome plays a significant role, along with the variance of the estimators, the distribution of the potential outcomes and the underlying costs and benefits of the treatment and the outcome.

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment

Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect of some action, or treatment, on the outcome of an individual. This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. First, we derive some original bounds on the probability of counterfactuals and we show that tightness of such bounds depends on the information of the feature set on the uplift term. Then, we propose a point estimator based on the assumption of conditional independence between the counterfactual outcomes. The quality of the bounds and the point estimators are assessed on synthetic data and a large real-world customer data set provided by a telecom company, showing significant improvement over the state of the art.