Learning Causal Effects on Hypergraphs

TL;DR

This paper introduces a causality learning framework using hypergraph neural networks to estimate individual treatment effects in complex multi-way interactions, addressing limitations of existing pairwise interference assumptions.

Contribution

It presents a novel framework for causal inference on hypergraphs that models high-order interference, improving ITE estimation accuracy in real-world group interaction scenarios.

Findings

Outperforms existing baselines on real-world hypergraph datasets.

Effectively models high-order interference in treatment effect estimation.

Demonstrates the importance of considering group interactions in causal analysis.

Abstract

Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we focus on the problem of individual treatment effect (ITE) estimation on hypergraphs, aiming to estimate how much an intervention (e.g., wearing face covering) would causally affect an outcome (e.g., COVID-19 infection) of each individual node. Existing works on ITE estimation either assume that the outcome on one individual should not be influenced by the treatment assignments on other individuals (i.e., no interference), or assume the interference only exists between pairs of connected individuals in an ordinary graph. We argue that these assumptions can be unrealistic on real-world hypergraphs, where higher-order interference can affect the ultimate ITE estimations due to the presence of group interactions. In this work, we investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks. Extensive experiments on real-world hypergraphs verify the superiority of our framework over existing baselines.