Estimate Causal Effects of Entangled Treatment on Graphs using Disentangled Instrumental Variables

Causal effect estimation on a graph of connected units is often complicated by entangled treatments, where the treatment assignment is not independent for each individual. This presents multiple challenges: accurately modeling treatment assignment mechanisms, adjusting for both observed and unobserved confounders to mitigate confounding bias, and constructing instrumental variables to adjust unobserved confounders within a graph structure. Prior research on estimating the causal effects of entangled treatments either assumed no unobserved confounders or relied on the manual selection of IVs, leading to gaps in the methodology. To bridge these gaps and build upon previous work, we introduce the Graph-Disentanglement Instrumental Variable (GDIV) model, a novel approach employing both Graph Neural Networks (GNNs) and Adversarial Networks to assess the causal effects on nodes in a graph, considering observed/unobserved confounders and the intricacies of treatment entanglement. Our GDIV estimator is validated through extensive experiments across synthetic and semisynthetic datasets, demonstrating its better performance over state-of-the-art methods. The ablation studies and robustness experiments verify the benefits of leveraging adversarial networks to generate IVs that satisfy the required assumptions.