  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.
