Maintenance is a challenging operational problem where the goal is to plan sufficient
preventive maintenance (PM) to avoid asset overhauls and failures. Existing work typically …

Individual treatment effect models allow optimizing decision-making by predicting the effect
of a treatment on an outcome of interest for individual instances. These predictions allow …

Measuring the success of targeted marketing actions is challenging. Research on
prescriptive analytics recommends uplift models to guide targeting decisions. Uplift models …

In this study, we present a novel cost-sensitive approach for uplift modeling in the context of
cross-selling and workforce analytics. We leverage referrals from sales agents across …

Despite the growing popularity of machine-learning techniques in decision-making, the
added value of causal-oriented strategies with respect to pure machine-learning …

Efficiently allocating treatments with a budget constraint constitutes an important challenge
across various domains. In marketing, for example, the use of promotions to target potential …

As a primary goal of predictive analytics, uplift modeling is used to estimate what impact a
specific action or treatment will have on an outcome. In convention, the treatment is …

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 …

In Influence Maximization (IM), the objective is to--given a budget--select the optimal set of
entities in a network to target with a treatment so as to maximize the total effect. For instance …

Recent developments in causal machine learning open perspectives for new approaches
that support decision-making in healthcare processes using causal models. In particular …