[2501.16562] C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference

Computer Science > Machine Learning arXiv:2501.16562 (cs) [Submitted on 27 Jan 2025 (v1), last revised 20 Mar 2026 (this version, v2)] Title:C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference Authors:Abhishek Dalvi, Neil Ashtekar, Vasant Honavar View a PDF of the paper titled C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference, by Abhishek Dalvi and 2 other authors View PDF HTML (experimental) Abstract: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 introduce a novel matching-based approach that utilizes principles from hyperdimensional computing to effectively encode and incorporate structural network information. This enables more accurate identification of comparable individuals, thereby improving the reliability of causal effect estimates. Through extensive empirical evaluation on multiple benchmark datasets, we demonstrate that our method either outperforms or performs on par with existing state-of-the-art approaches, including several recent deep learning-base...