Causal graph transformer for treatment effect estimation under unknown interference
Wu, Anpeng Qiu, Haiyi Chen, Zhengming Li, Zijian Xiong, Ruoxuan Wu, Fei Zhang, Kun
Wu, Anpeng
Qiu, Haiyi
Chen, Zhengming
Li, Zijian
Xiong, Ruoxuan
Wu, Fei
Zhang, Kun
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Department
Machine Learning
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Conference proceeding
Date
2025
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English
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Abstract
Networked interference, also known as the peer effect in social science and spillover effect in economics, has drawn increasing interest across various domains. This phenomenon arises when a unit's treatment and outcome are influenced by the actions of its peers, posing significant challenges to causal inference, particularly in treatment assignment and effect estimation in real applications, due to the violation of the SUTVA assumption. While extensive graph models have been developed to identify treatment effects, these models often rely on structural assumptions about networked interference, assuming it to be identical to the social network, which can lead to misspecification issues in real applications. To address these challenges, we propose an Interference-Agnostic Causal Graph Transformer (CauGramer), which aggregates peers information via L-order Graph Transformer and employs cross-attention to infer aggregation function for learning interference representations. By integrating confounder balancing and minimax moment constraints, CauGramer fully incorporates peer information, enabling robust treatment effect estimation. Extensive experiments on two widely-used benchmarks demonstrate the effectiveness and superiority of CauGramer. The code is available at https://github.com/anpwu/CauGramer. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
A. Wu et al., “Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference,” International Conference on Representation Learning, vol. 2025, pp. 99579–99608, May 2025
Source
13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR