Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

1 code implementation • 21 Mar 2024 • Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong, Bo Li, Xiaoqing Yang, Xuan Qin, Peng Zhen, Jiecheng Guo, Fei Wu, Kun Kuang

Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science.

counterfactual Decision Making +2

Long-term Causal Effects Estimation via Latent Surrogates Representation Learning

1 code implementation • 9 Aug 2022 • Ruichu Cai, Weilin Chen, Zeqin Yang, Shu Wan, Chen Zheng, Xiaoqing Yang, Jiecheng Guo

Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e. g., marketing and medicine.

Marketing Representation Learning +1

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.