Search Results for author: Anpeng Wu

Found 10 papers, 6 papers with code

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

1 code implementation21 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

Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects

no code implementations5 Mar 2024 Yingrong Wang, Anpeng Wu, Haoxuan Li, Weiming Liu, Qiaowei Miao, Ruoxuan Xiong, Fei Wu, Kun Kuang

This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other.

Representation Learning

Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series

no code implementations16 Aug 2023 Anpeng Wu, Haoxuan Li, Kun Kuang, Keli Zhang, Fei Wu

Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges.

Time Series

Instrumental Variables in Causal Inference and Machine Learning: A Survey

1 code implementation12 Dec 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Fei Wu

This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning.

Causal Inference

Confounder Balancing for Instrumental Variable Regression with Latent Variable

no code implementations18 Nov 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu

This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.

regression valid

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

1 code implementation23 Aug 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).

regression

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.

Cloud Computing Edge-computing

Treatment effect estimation with confounder balanced instrumental variable regression

no code implementations29 Sep 2021 Anpeng Wu, Kun Kuang, Fei Wu

In this paper, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders with IV regression and reduce the bias from the observed confounders by balancing for treatment effect estimation.

regression

Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition

1 code implementation13 Jul 2021 Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, Lanfen Lin

We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome.

Causal Inference counterfactual +1

Learning Decomposed Representation for Counterfactual Inference

1 code implementation12 Jun 2020 Anpeng Wu, Kun Kuang, Junkun Yuan, Bo Li, Runze Wu, Qiang Zhu, Yueting Zhuang, Fei Wu

The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.

counterfactual Counterfactual Inference

Cannot find the paper you are looking for? You can Submit a new open access paper.