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Instrumental Variable Regression with Confounder Balancing
Anpeng Wu · Kun Kuang · Bo Li · Fei Wu

Tue Jul 19 02:15 PM -- 02:20 PM (PDT) @ Ballroom 3 & 4
in MISC: Causality »

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.

Keywords: MISC: Causality · MISC: Representation Learning

Author Information

Anpeng Wu (Zhejiang University)
Kun Kuang (Zhejiang University)
Kun Kuang

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)

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