Abstract: Highlights•We introduce a cost-sensitive decision-making framework for causal classification.•We derive the classification boundary to maximize the expected causal profit.•We establish a new cost-sensitive ranking approach with individual treatment effects.•Experiments demonstrate an increase in profit compared to cost-insensitive ranking.
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