C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference

ArXi:2501.16562v2 Announce Type: replace We address the problem of estimating causal effects from observational data in the presence of network confounding, a setting where both treatment assignment and observed outcomes of individuals may be influenced by their neighbors within a network structure, resulting in network interference. Traditional causal inference methods often fail to account for these dependencies, leading to biased estimates. To tackle this challenge, we