The goal of causal classification is to identify individuals whose outcome would be positively changed by a treatment. Examples include targeting advertisements and targeting retention incentives to reduce churn. Causal classification is challenging because we observe individuals under only one...

Most induction algorithms for building predictive models take as input training data in the form of feature vectors. Acquiring the values of features may be costly, and simply acquiring all values may be wasteful, or even prohibitively expensive. Active feature-value acquisition (AFA) elects...

Dimensionality Reduction (DR) is frequently employed in the predictive modeling process with the goal of improving the generalization performance of models. This paper takes a design science perspective on DR.We treat it as an important business analytics artifact and investigate its utility in...