Timezone: »
This paper considers the challenge of estimating treatment effects from observational data in the presence of unmeasured confounders. A popular way to address this challenge is to utilize an instrumental variable (IV) for two-stage regression, i.e., 2SLS and variants, but limited to the linear setting. Recently, many nonlinear IV regression variants were proposed to overcome it by regressing the treatment with IVs and observed confounders in stage 1, leading to the imbalance of the observed confounders in stage 2. In this paper, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders and balance the observed confounders. To the best of our knowledge, this is the first work to combine confounder balancing in IV regression for treatment effect estimation. Theoretically, we re-define and solve the inverse problems for the response-outcome function. Experiments show that our algorithm outperforms the existing approaches.
Author Information
Anpeng Wu (Zhejiang University)
Kun Kuang (Zhejiang University)

Kun Kuang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University. He received his Ph.D. in the Department of Computer Science and Technology at Tsinghua University in 2019. He was a visiting scholar with Prof. Susan Athey's Group at Stanford University. His main research interests include Causal Inference, Data Mining, and Causality Inspired Machine Learning. He has published over 70 papers in prestigious conferences and journals in data mining and machine learning, including TKDE, TPAMI, ICML, NeurIPS, KDD, ICDE, WWW, MM, DMKD, Engineering, etc. He received ACM SIGAI China Rising Star Award in 2022.
Bo Li (Tsinghua University)
Fei Wu (Zhejiang University, China)
Related Events (a corresponding poster, oral, or spotlight)
-
2022 Poster: Instrumental Variable Regression with Confounder Balancing »
Tue. Jul 19th through Wed the 20th Room Hall E #608
More from the Same Authors
-
2022 : Towards Multi-level Fairness and Robustness on Federated Learning »
Fengda Zhang · Kun Kuang · Yuxuan Liu · Long Chen · Jiaxun Lu · Yunfeng Shao · Fei Wu · Chao Wu · Jun Xiao -
2023 Poster: Competing for Shareable Arms in Multi-Player Multi-Armed Bandits »
Renzhe Xu · Haotian Wang · Xingxuan Zhang · Bo Li · Peng Cui -
2023 Poster: Stable Estimation of Heterogeneous Treatment Effects »
Anpeng Wu · Kun Kuang · Ruoxuan Xiong · Bo Li · Fei Wu -
2023 Poster: Causal Structure Learning for Latent Intervened Non-stationary Data »
Chenxi Liu · Kun Kuang -
2022 Poster: Counterfactual Prediction for Outcome-Oriented Treatments »
Hao Zou · Bo Li · Jiangang Han · Shuiping Chen · Xuetao Ding · Peng Cui -
2022 Spotlight: Counterfactual Prediction for Outcome-Oriented Treatments »
Hao Zou · Bo Li · Jiangang Han · Shuiping Chen · Xuetao Ding · Peng Cui -
2022 Poster: The Role of Deconfounding in Meta-learning »
Yinjie Jiang · Zhengyu Chen · Kun Kuang · Luotian Yuan · Xinhai Ye · Zhihua Wang · Fei Wu · Ying WEI -
2022 Poster: Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning »
Jiahui Li · Kun Kuang · Baoxiang Wang · Furui Liu · Long Chen · Changjie Fan · Fei Wu · Jun Xiao -
2022 Spotlight: Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning »
Jiahui Li · Kun Kuang · Baoxiang Wang · Furui Liu · Long Chen · Changjie Fan · Fei Wu · Jun Xiao -
2022 Spotlight: The Role of Deconfounding in Meta-learning »
Yinjie Jiang · Zhengyu Chen · Kun Kuang · Luotian Yuan · Xinhai Ye · Zhihua Wang · Fei Wu · Ying WEI -
2021 Poster: KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation »
Haozhe Feng · Zhaoyang You · Minghao Chen · Tianye Zhang · Minfeng Zhu · Fei Wu · Chao Wu · Wei Chen -
2021 Spotlight: KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation »
Haozhe Feng · Zhaoyang You · Minghao Chen · Tianye Zhang · Minfeng Zhu · Fei Wu · Chao Wu · Wei Chen -
2021 Poster: Heterogeneous Risk Minimization »
Jiashuo Liu · Zheyuan Hu · Peng Cui · Bo Li · Zheyan Shen -
2021 Spotlight: Heterogeneous Risk Minimization »
Jiashuo Liu · Zheyuan Hu · Peng Cui · Bo Li · Zheyan Shen -
2021 Poster: Explainable Automated Graph Representation Learning with Hyperparameter Importance »
Xin Wang · Shuyi Fan · Kun Kuang · Wenwu Zhu -
2021 Spotlight: Explainable Automated Graph Representation Learning with Hyperparameter Importance »
Xin Wang · Shuyi Fan · Kun Kuang · Wenwu Zhu -
2020 Poster: Description Based Text Classification with Reinforcement Learning »
Duo Chai · Wei Wu · Qinghong Han · Fei Wu · Jiwei Li -
2019 Poster: Disentangled Graph Convolutional Networks »
Jianxin Ma · Peng Cui · Kun Kuang · Xin Wang · Wenwu Zhu -
2019 Oral: Disentangled Graph Convolutional Networks »
Jianxin Ma · Peng Cui · Kun Kuang · Xin Wang · Wenwu Zhu