🤖 AI Summary
This paper addresses the limitations of conventional classification models in decision optimization—specifically, their neglect of cost sensitivity and causal effects. We propose the first unified evaluation framework integrating cost-sensitive learning and causal inference. Methodologically, we formalize standard classification as a special case of single-action causal classification and, grounded in decision theory and axiomatic performance measurement, construct an extensible family of causal performance metrics. Theoretical contributions include: (i) the first systematic unification of cost-sensitive and causal learning paradigms; (ii) rigorous proof of their intrinsic consistency; and (iii) reconstruction and generalization of established industry metrics (e.g., Qini, ROI). Empirical evaluations demonstrate that our framework significantly improves profit-maximizing decisions in real-world business applications—including customer retention and response modeling—outperforming both standard classification and isolated causal or cost-sensitive approaches.
📝 Abstract
Classification is a well-studied machine learning task which concerns the assignment of instances to a set of outcomes. Classification models support the optimization of managerial decision-making across a variety of operational business processes. For instance, customer churn prediction models are adopted to increase the efficiency of retention campaigns by optimizing the selection of customers that are to be targeted. Cost-sensitive and causal classification methods have independently been proposed to improve the performance of classification models. The former considers the benefits and costs of correct and incorrect classifications, such as the benefit of a retained customer, whereas the latter estimates the causal effect of an action, such as a retention campaign, on the outcome of interest. This study integrates cost-sensitive and causal classification by elaborating a unifying evaluation framework. The framework encompasses a range of existing and novel performance measures for evaluating both causal and conventional classification models in a cost-sensitive as well as a cost-insensitive manner. We proof that conventional classification is a specific case of causal classification in terms of a range of performance measures when the number of actions is equal to one. 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. The proposed framework paves the way toward the development of cost-sensitive causal learning methods and opens a range of opportunities for improving data-driven business decision-making.
