Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

Excerpt

arXiv:2502.19741v4 Announce Type: replace 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 lev