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

TL;DR

This paper introduces C-HDNet, a hyperdimensional computing-based matching method for causal effect estimation in networked observational data, achieving high accuracy and efficiency in large-scale settings.

Contribution

It presents a novel hyperdimensional encoding approach for network confounding, improving causal inference accuracy and computational efficiency over existing methods.

Findings

Outperforms or matches state-of-the-art methods on benchmark datasets.

Achieves nearly tenfold reduction in runtime compared to deep learning models.

Effectively handles large-scale network data with improved accuracy.

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-based models that are significantly more computationally intensive. In addition to its strong empirical performance, our method offers substantial practical advantages, achieving nearly an order-of-magnitude reduction in runtime without compromising accuracy, making it particularly well-suited for large-scale or time-sensitive application