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Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments by Zhu, Hongtao; Zhang, Sizhe; Su, Yang; Zhao, Zhenyu; Chen, Nan is a scholarly article available to read on EtoBox.

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In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment a

Author

Zhu, Hongtao; Zhang, Sizhe; Su, Yang; Zhao, Zhenyu; Chen, Nan

Published

2024

Language

EN