GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
广义因果森林:在线市场中的异质性处理效应估计
Uplift modeling is a rapidly growing approach that utilizes causal inference and machine learning methods to directly estimate the heterogeneous treatment effects, which has been widely applied to various online marketplaces to assist large-scale decision-making in recent years. The existing popular models, like causal forest (CF), are limited to either discrete treatments or posing parametric assumptions on the outcome-treatment relationship that may suffer model…