Causal Influence Maximization in Hypergraph Networks with Unknown Individual Treatment Effects

The core message of this paper is to introduce a new framework called Causal Influence Maximization (CauIM) that aims to find the seed set that maximizes the expected sum of the causal effects (individual treatment effects) of the infected nodes in a hypergraph network.