Search Results for author:
Found 2 papers, 0 papers with code
To do or not to do: cost-sensitive causal decision-making
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 foundations of cost-sensitive causal classification
The framework is shown to instantiate to application-specific cost-sensitive performance measures that have been recently proposed for evaluating customer retention and response uplift models, and allows to maximize profitability when adopting a causal classification model for optimizing decision-making.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
