Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

TL;DR

This paper introduces a novel method for estimating causal effects in networked data without relying on the often violated unconfoundedness assumption, by recovering latent confounders through interaction patterns.

Contribution

It proposes a confounder recovery framework that identifies three types of latent confounders and develops a network effect estimator using identifiable representation learning.

Findings

The method successfully recovers latent confounders affecting units and neighbors.

The approach achieves accurate causal effect estimation without the unconfoundedness assumption.

Experimental results validate the theoretical identifiability and effectiveness of the proposed method.

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.