论文标题
部分反事实识别和隆升建模：理论结果和现实世界评估
Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
论文作者
论文摘要
反事实是因果人类推理和科学发现过程中的核心。升高（也称为条件平均治疗效应）衡量某些作用或治疗对个体结果的因果效应。本文讨论了如何根据隆起术语来得出反事实语句的概率的范围。首先，我们在反事实的概率上得出了一些原始界限，我们表明此类界限的紧密度取决于提升术语设置的功能的信息。然后，我们根据反事实结果之间有条件独立性的假设提出了一个点估计器。对综合数据和电信公司提供的大型现实世界客户数据集进行了界限和点估计器的质量，显示出对最新技术状态的显着改善。
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.
