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

TL;DR

This paper introduces a new method for estimating causal effects in observational data with network interference, using hyperdimensional computing to improve accuracy and efficiency.

Contribution

A novel matching-based approach using hyperdimensional computing to encode network structure for causal effect estimation.

Findings

The method outperforms or matches state-of-the-art approaches in causal effect estimation.

It achieves significant runtime improvements without sacrificing accuracy.

It is well-suited for large-scale or time-sensitive applications.

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 applications.

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