Telling Peer Direct Effects from Indirect Effects in Observational Network Data
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(Published version)
Date
2025
Authors
Du, X.
Li, J.
Cheng, D.
Liu, L.
Gao, W.
Chen, X.
Xu, Z.
Editors
Singh, A.
Fazel, M.
Hsu, D.
Lacoste-Julien, S.
Berkenkamp, F.
Maharaj, T.
Wagstaff, K.
Zhu, J.
Fazel, M.
Hsu, D.
Lacoste-Julien, S.
Berkenkamp, F.
Maharaj, T.
Wagstaff, K.
Zhu, J.
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Conference paper
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Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), as puiblished in Proceedings of Machine Learning Research, 2025 / Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. (ed./s), vol.267, pp.14562-14578
Statement of Responsibility
Xiaojing Du, Jiuyong Li, Debo Cheng, Lin Liu, Wentao Gao, Xiongren Chen, Ziqi Xu
Conference Name
International Conference on Machine Learning (ICML) (13 Jul 2025 - 19 Jul 2025 : Vancouver, Canada)
Abstract
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Some algorithms have been proposed to estimate causal effects involving network data, particularly peer effects, but they often fail to tell apart diverse peer effects. To address this issue, we propose a general setting which considers both peer direct effects and peer indirect effects, and the effect of an individual's own treatment, and provide the identification conditions of these causal effects. To differentiate these effects, we leverage causal mediation analysis and tailor it specifically for network data. Furthermore, given the inherent challenges of accurately estimating effects in networked environments, we propose to incorporate attention mechanisms to capture the varying influences of different neighbors and to explore high-order neighbor effects using multi-layer graph neural networks (GNNs). Additionally, we employ the Hilbert-Schmidt Independence Criterion (HSIC) to further enhance the model’s robustness and accuracy. Extensive experiments on two semi-synthetic datasets derived from real-world networks and on a dataset from a recommendation system confirm the effectiveness of our approach. Our findings have the potential to improve intervention strategies in networked systems, particularly in social networks and public health.
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Copyright 2025 by the author(s).