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

Abstract

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.

References in corpus (5)

• Recursive Partitioning for Heterogeneous Causal Effects
• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
• Partial Counterfactual Identification from Observational and Experimental Data
• Nested Counterfactual Identification from Arbitrary Surrogate Experiments