Search Results for author: Wouter Verbeke

Found 23 papers, 11 papers with code

Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation

1 code implementation12 Jun 2024 Christopher Bockel-Rickermann, Toon Vanderschueren, Tim Verdonck, Wouter Verbeke

We apply this scheme to eight popular CADR estimators on four widely-used benchmark datasets, running nearly 1, 500 individual experiments.

Uplift vs. predictive modeling: a theoretical analysis

1 code implementation21 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.

Decision Making Marketing

A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions

no code implementations7 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.

Causal Inference Selection bias

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time

1 code implementation7 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.

Timing Process Interventions with Causal Inference and Reinforcement Learning

no code implementations7 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.

Causal Inference reinforcement-learning +1

Client Recruitment for Federated Learning in ICU Length of Stay Prediction

1 code implementation28 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.

Federated Learning Length-of-Stay prediction

Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field

no code implementations7 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.

Fraud Detection

Prescriptive maintenance with causal machine learning

no code implementations3 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.

BIG-bench Machine Learning Causal Inference

A new perspective on classification: optimally allocating limited resources to uncertain tasks

no code implementations9 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.

Fraud Detection Learning-To-Rank

To do or not to do: cost-sensitive causal decision-making

no code implementations5 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.

Classification Decision Making +2

Weight-of-evidence 2.0 with shrinkage and spline-binning

1 code implementation5 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.

Decision Making Fraud Detection

HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks

no code implementations26 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.

Multi-Task Learning

The foundations of cost-sensitive causal classification

no code implementations24 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.

Classification Decision Making +1

Misclassification cost-sensitive ensemble learning: A unifying framework

no code implementations14 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.

Ensemble Learning

Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud

1 code implementation5 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

Autoencoders for strategic decision support

no code implementations3 May 2020 Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke

However, few data-driven tools that support strategic decision-making are available.

Decision Making

Learning to rank for uplift modeling

no code implementations14 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.

Learning-To-Rank Marketing

Optimising Individual-Treatment-Effect Using Bandits

1 code implementation16 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.

Causal Inference Marketing

Causal Simulations for Uplift Modeling

1 code implementation1 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.

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