1 code implementation • 21 Sep 2023 • Théo Verhelst, Robin Petit, Wouter Verbeke, Gianluca Bontempi
Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature.
no code implementations • 7 Sep 2023 • Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke
In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making.
1 code implementation • 7 Sep 2023 • Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke
In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data.
1 code implementation • 7 Jun 2023 • Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment strategies in many practical applications.
no code implementations • 7 Jun 2023 • Hans Weytjens, Wouter Verbeke, Jochen De Weerdt
Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results.
1 code implementation • 28 Apr 2023 • Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor
In this work, we address the step prior to the initiation of a federated network for model training, client recruitment.
no code implementations • 7 Dec 2022 • Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke
In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research.
no code implementations • 3 Jun 2022 • Toon Vanderschueren, Robert Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke
This work proposes to relax both assumptions by learning the effect of maintenance conditional on a machine's characteristics from observational data on similar machines using existing methodologies for causal inference.
no code implementations • 9 Feb 2022 • Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain.
no code implementations • 5 Jan 2021 • Diego Olaya, Wouter Verbeke, Jente Van Belle, Marie-Anne Guerry
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.
1 code implementation • 5 Jan 2021 • Jakob Raymaekers, Wouter Verbeke, Tim Verdonck
We present the results of a series of experiments in a fraud detection setting, which illustrate the effectiveness of the presented approach.
no code implementations • 26 Aug 2020 • Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Wouter Verbeke
Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks.
no code implementations • 24 Jul 2020 • Wouter Verbeke, Diego Olaya, Jeroen Berrevoets, Sam Verboven, Sebastián Maldonado
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.
no code implementations • 14 Jul 2020 • George Petrides, Wouter Verbeke
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs.
1 code implementation • 5 May 2020 • Sebastiaan Höppner, Bart Baesens, Wouter Verbeke, Tim Verdonck
Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model.
Applications
no code implementations • 3 May 2020 • Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke
However, few data-driven tools that support strategic decision-making are available.
no code implementations • 14 Feb 2020 • Floris Devriendt, Tias Guns, Wouter Verbeke
We propose a unified formalisation of different global uplift modeling measures in use today and explore how these can be integrated into the learning-to-rank framework.
1 code implementation • 16 Oct 2019 • Jeroen Berrevoets, Sam Verboven, Wouter Verbeke
Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community.
1 code implementation • 1 Feb 2019 • Jeroen Berrevoets, Wouter Verbeke
Hence, methods are being developed that are able to learn from newly gained experience, as well as handle drifting environments.