无需网络无混杂假设的网络干扰下因果效应估计

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Abstract:Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly rely on the networked unconfoundedness assumption, which guarantees the identification of networked effects. However, this assumption is often violated due to the latent confounders inherent in observational data, thereby hindering the identification of networked effects. To address this issue, we leverage the rich interaction patterns between units in networks, which provide valuable information for recovering these latent confounders. Building on this insight, we develop a confounder recovery framework that explicitly characterizes three categories of latent confounders in networked settings: those affecting only the unit, those affecting only the unit's neighbors, and those influencing both. Based on this framework, we design a networked effect estimator using identifiable representation learning techniques. From a theoretical standpoint, we prove the identifiability of all three types of latent confounders and, by leveraging the recovered confounders, establish a formal identification result for networked effects. Extensive experiments validate our theoretical findings and demonstrate the effectiveness of the proposed method.

Comments:	accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, in press
Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2502.19741 [cs.LG]
	(or arXiv:2502.19741v4 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2502.19741 arXiv-issued DOI via DataCite

Submission history

From: Weilin Chen [view email]
[v1] Thu, 27 Feb 2025 04:07:32 UTC (748 KB)
[v2] Sat, 2 Aug 2025 08:08:56 UTC (1,242 KB)
[v3] Tue, 27 Jan 2026 03:54:28 UTC (1,484 KB)
[v4] Tue, 6 Oct 2026 13:20:31 UTC (6,906 KB)