Wouter Verbeke — accepted AI-conference papers

2025

AutoCATE: End-to-End, Automated Treatment Effect Estimation

Toon Vanderschueren, Tim Verdonck, Mihaela van der Schaar, Wouter Verbeke

ICML 2025poster

Estimating causal effects is crucial in domains like healthcare, economics, and education. Despite advances in machine learning (ML) for estimating conditional average treatment effects (CATE), the practical adoption of these methods remains limited, due to the complexities of implementing, tuning,…

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2023

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

Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar

ICML 2023poster

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. However, a significant challenge that has been largely overlooked by the ML lite…

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