Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
This work addresses causal inference challenges in domains like marketing and policy-making, offering incremental advances in uplift modeling.
The paper tackles the problem of estimating counterfactual probabilities and uplift by deriving theoretical bounds and proposing a point estimator, showing significant improvement over state-of-the-art methods on synthetic and real-world telecom data.
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.