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Causal classification models are adopted across a variety of operationalbusiness processes to predict the effect of a treatment on a categoricalbusiness outcome of interest depending on the process instance characteristics.This allows optimizing operational decision-making and selecting the optimaltreatment to apply in each specific instance, with the aim of maximizing thepositive outcome rate. While various powerful approaches have been presented inthe literature for learning causal classification models, no formal frameworkhas been elaborated for optimal decision-making based on the estimatedindividual treatment effects, given the cost of the various treatments and thebenefit of the potential outcomes.  In this article, we therefore extend upon the expected value framework andformally introduce a cost-sensitive decision boundary for double binary causalclassification, which is a linear function of the estimated individualtreatment effect, the positive outcome probability and the cost and benefitparameters of the problem setting. The boundary allows causally classifyinginstances in the positive and negative treatment class to maximize the expectedcausal profit, which is introduced as the objective at hand in cost-sensitivecausal classification. We introduce the expected causal profit ranker whichranks instances for maximizing the expected causal profit at each possiblethreshold for causally classifying instances and differs from the conventionalranking approach based on the individual treatment effect. The proposed rankingapproach is experimentally evaluated on synthetic and marketing campaign datasets. The results indicate that the presented ranking method effectivelyoutperforms the cost-insensitive ranking approach and allows boostingprofitability.

To do or not to do: cost-sensitive causal decision-making