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Causal Influence Maximization with Steady-State Guarantees
Classical influence maximization optimizes expected reach, which can be misaligned with welfare when exposures have heterogeneous, saturating, or adverse effects. We study seed selection for steady-state causal welfare under network interference. Under our exposure-separable outcome model and weak i
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