Learning Causal Effects on Hypergraphs. (arXiv:2207.04049v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2207.04049

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