A framework for causal segmentation analysis with machine learning in large-scale digital experiments
大型数字实验中基于机器学习的因果分群分析框架
We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digital experiments. Building on recent developments in causal inference and non/semi-parametric statistics, our approach unifies two objectives: (1) the discovery of user segments that stand to benefit from a candidate treatment based on subgroup-specific treatment effects, and (2) the evaluation of…