  Causal classification models are adopted across a variety of operational
business processes to predict the effect of a treatment on a categorical
business outcome of interest depending on the process instance characteristics.
This allows optimizing operational decision-making and selecting the optimal
treatment to apply in each specific instance, with the aim of maximizing the
positive outcome rate. While various powerful approaches have been presented in
the literature for learning causal classification models, no formal framework
has been elaborated for optimal decision-making based on the estimated
individual treatment effects, given the cost of the various treatments and the
benefit of the potential outcomes.
  In this article, we therefore extend upon the expected value framework and
formally introduce a cost-sensitive decision boundary for double binary causal
classification, which is a linear function of the estimated individual
treatment effect, the positive outcome probability and the cost and benefit
parameters of the problem setting. The boundary allows causally classifying
instances in the positive and negative treatment class to maximize the expected
causal profit, which is introduced as the objective at hand in cost-sensitive
causal classification. We introduce the expected causal profit ranker which
ranks instances for maximizing the expected causal profit at each possible
threshold for causally classifying instances and differs from the conventional
ranking approach based on the individual treatment effect. The proposed ranking
approach is experimentally evaluated on synthetic and marketing campaign data
sets. The results indicate that the presented ranking method effectively
outperforms the cost-insensitive ranking approach and allows boosting
profitability.
