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Influence Maximization (IM) is the task of selecting a fixed number of seednodes in a given network to maximize dissemination benefits. Although theresearch for efficient algorithms has been dedicated recently, it is usuallyneglected to further explore the graph structure and the objective functioninherently. With this motivation, we take the first attempt on thehypergraph-based IM with a novel causal objective. We consider the case thateach hypergraph node carries specific attributes with Individual TreatmentEffect (ITE), namely the change of potential outcomes before/after infectionsin a causal inference perspective. In many scenarios, the sum of ITEs of theinfected is a more reasonable objective for influence spread, whereas it isdifficult to achieve via current IM algorithms. In this paper, we introduce anew algorithm called \textbf{CauIM}. We first recover the ITE of each node withobservational data and then conduct a weighted greedy algorithm to maximize thesum of ITEs of the infected. Theoretically, we mainly present the generalizedlower bound of influence spread beyond the well-known $(1-\frac{1}{e})$ optimalguarantee and provide the robustness analysis. Empirically, in real-worldexperiments, we demonstrate the effectiveness and robustness of \textbf{CauIM}.It outperforms the previous IM and randomized methods significantly.

Causal Influence Maximization in Hypergraph