GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace

no code implementations • 21 Mar 2022 • Shu Wan, Chen Zheng, Zhonggen Sun, Mengfan Xu, Xiaoqing Yang, Hongtu Zhu, Jiecheng Guo

We show the effectiveness of GCF by deriving the asymptotic property of the estimator and comparing it to popular uplift modeling methods on both synthetic and real-world datasets.

Causal Inference Decision Making

GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods

no code implementations • 29 Sep 2021 • Shu Wan, Chen Zheng, Zhonggen Sun, Mengfan Xu, Xiaoqing Yang, Jiecheng Guo, Hongtu Zhu

Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry.