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Stable Estimation of Heterogeneous Treatment Effects
Anpeng Wu · Kun Kuang · Ruoxuan Xiong · Bo Li · Fei Wu

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #411
Event URL: https://github.com/anpwu/StableCFR »

Estimating heterogeneous treatment effects (HTE) is crucial for identifying the variation of treatment effects across individuals or subgroups. Most existing methods estimate HTE by removing the confounding bias from imbalanced treatment assignments. However, these methods may produce unreliable estimates of treatment effects and potentially allocate suboptimal treatment arms for underrepresented populations. To improve the estimation accuracy of HTE for underrepresented populations, we propose a novel Stable CounterFactual Regression (StableCFR) to smooth the population distribution and upsample the underrepresented subpopulations, while balancing confounders between treatment and control groups. Specifically, StableCFR upsamples the underrepresented data using uniform sampling, where each disjoint subpopulation is weighted proportional to the Lebesgue measure of its support. Moreover, StableCFR balances covariates by using an epsilon-greedy matching approach. Empirical results on both synthetic and real-world datasets demonstrate the superior performance of our StableCFR on estimating HTE for underrepresented populations.

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.

Ruoxuan Xiong (Stanford University)
Bo Li (Tsinghua University)
Fei Wu (Zhejiang University, China)

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