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

TL;DR

This paper develops theoretical bounds and estimators for counterfactual probabilities using uplift modeling, validated on synthetic and real-world data, advancing causal inference methods.

Contribution

It introduces new bounds on counterfactual probabilities based on uplift, and proposes a point estimator under conditional independence assumptions, with empirical validation.

Findings

Bounds depend on feature information about uplift

Proposed estimators outperform existing methods

Validated on synthetic and telecom data

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.