The identification of a seed set to maximize information spread in a network is crucial, a concept known as Influence Maximization (IM). Elegant IM algorithms could naturally extend to cases where each node is equipped with specific weight, referred to as individual effect, to measure the node's importance. Prevailing literature has typically assumed that the individual effect remains constant during the cascade process. However, this assumption is not always feasible, as the individual effect of each node is primarily evaluated by the difference between the outputs in the activated and non-activated states, with one of these states always being unobservable after propagation. Moreover, the individual effect is sensitive to the environmental information provided by surrounding nodes. To address these challenges, we extend the consideration of IM to a broader scenario involving general networks with dynamic node individual effects, leveraging causality techniques. In our paper, we address this through the development of a Causal Influence Maximization (CauIM) algorithm. Theoretically, for CauIM, we present the generalized lower bound of influence spread and provide robustness analysis. Empirically, in synthetic and real-world experiments, we demonstrate the effectiveness and robustness of CauIM, along with a novel acceleration technique.

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