Causal Simulations for Uplift Modeling. - Jeroen Berrevoets; Wouter Verbeke (CoRR, 2019)

Optimising Individual-Treatment-Effect Using Bandits. - Jeroen Berrevoets; Sam Verboven; Wouter Verbeke (CoRR, 2019)

OrganITE: Optimal transplant donor organ offering using an individual treatment effect. - Jeroen Berrevoets; James Jordon; Ioana Bica; Alexander Gimson; Mihaela van der Schaar (NeurIPS, 2020)

The foundations of cost-sensitive causal classification. - Wouter Verbeke; Diego Olaya; Jeroen Berrevoets; Sebastián Maldonado (CoRR, 2020)

HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks. - Sam Verboven; Muhammad Hafeez Chaudhary; Jeroen Berrevoets; Wouter Verbeke (CoRR, 2020)

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis. - Jeroen Berrevoets; Ahmed M. Alaa; Zhaozhi Qian; James Jordon; Alexander E. S. Gimson; Mihaela van der Schaar (ICML, 2021)

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. - Boris van Breugel; Trent Kyono; Jeroen Berrevoets; Mihaela van der Schaar (NeurIPS, 2021)

Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time. - Jeroen Berrevoets; Alicia Curth; Ioana Bica; Eoin F. McKinney; Mihaela van der Schaar (CoRR, 2021)

Autoencoders for strategic decision support. - Sam Verboven; Jeroen Berrevoets; Chris Wuytens; Bart Baesens; Wouter Verbeke (Decis. Support Syst., 2021)

Why you should stop predicting customer churn and start using uplift models. - Floris Devriendt; Jeroen Berrevoets; Wouter Verbeke (Inf. Sci., 2021)

Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. - Yao Zhang; Jeroen Berrevoets; Mihaela van der Schaar (AISTATS, 2022)

Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. - Tobias Hatt; Jeroen Berrevoets; Alicia Curth; Stefan Feuerriegel; Mihaela van der Schaar (CoRR, 2022)

Navigating causal deep learning. - Jeroen Berrevoets; Krzysztof Kacprzyk; Zhaozhi Qian; Mihaela van der Schaar (CoRR, 2022)

To Impute or not to Impute? Missing Data in Treatment Effect Estimation. - Jeroen Berrevoets; Fergus Imrie; Trent Kyono; James Jordon; Mihaela van der Schaar (AISTATS, 2023)

GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. - Tennison Liu; Zhaozhi Qian; Jeroen Berrevoets; Mihaela van der Schaar (ICLR, 2023)

Differentiable and Transportable Structure Learning. - Jeroen Berrevoets; Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (ICML, 2023)

Learning Representations without Compositional Assumptions. - Tennison Liu; Jeroen Berrevoets; Zhaozhi Qian; Mihaela van der Schaar (ICML, 2023)

AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems. - Jeroen Berrevoets; Daniel Jarrett; Alex J. Chan; Mihaela van der Schaar (NeurIPS, 2023)

Causal Deep Learning. - Jeroen Berrevoets; Krzysztof Kacprzyk; Zhaozhi Qian; Mihaela van der Schaar (CoRR, 2023)

Learning continuous-valued treatment effects through representation balancing. - Christopher Bockel-Rickermann; Toon Vanderschueren; Jeroen Berrevoets; Tim Verdonck; Wouter Verbeke (CoRR, 2023)

NOFLITE: Learning to Predict Individual Treatment Effect Distributions. - Toon Vanderschueren; Jeroen Berrevoets; Wouter Verbeke (Trans. Mach. Learn. Res., 2023)

HydaLearn. - Sam Verboven; Muhammad Hafeez Chaudhary; Jeroen Berrevoets; Vincent Ginis; Wouter Verbeke (Appl. Intell., 2023)

DAGnosis: Localized Identification of Data Inconsistencies using Structures. - Nicolas Huynh; Jeroen Berrevoets; Nabeel Seedat; Jonathan Crabbé; Zhaozhi Qian; Mihaela van der Schaar (AISTATS, 2024)

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. - Krzysztof Kacprzyk; Samuel Holt; Jeroen Berrevoets; Zhaozhi Qian; Mihaela van der Schaar (ICLR, 2024)

DynamITE: Optimal time-sensitive organ offers using ITE. - Alessandro Marchese; Hans de Ferrante; Jeroen Berrevoets; Sam Verboven (ML4H@NeurIPS, 2024)

Machine Learning for Health (ML4H) 2024. - Helen Zhou; Stefan Hegselmann; Elizabeth Healey; Trenton Chang; Caleb Ellington; Michael Leone; Vishwali Mhasawade; Sana Tonekaboni; Winston Chen; Hyewon Jeong; Xiaoxiao Li; Juyeon Heo; et al. (ML4H@NeurIPS, 2024)

Optimizing Treatment Allocation in the Presence of Interference. - Daan Caljon; Jente Van Belle; Jeroen Berrevoets; Wouter Verbeke (CoRR, 2024)

Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality. - Jeroen Berrevoets; Krzysztof Kacprzyk; Zhaozhi Qian; Mihaela van der Schaar (Found. Trends Signal Process., 2024)

Differentiable Causal Structure Learning with Identifiability by NOTIME. - Jeroen Berrevoets; Jakob Raymaekers; Mihaela van der Schaar; Tim Verdonck; Ruicong Yao (AISTATS, 2025)

Active Feature Acquisition for Personalised Treatment Assignment. - Julianna Piskorz; Nicolás Astorga; Jeroen Berrevoets; Mihaela van der Schaar (AISTATS, 2025)

Using representation balancing to learn conditional-average dose responses from clustered data. - Christopher Bockel-Rickermann; Toon Vanderschueren; Jeroen Berrevoets; Tim Verdonck; Wouter Verbeke (Trans. Mach. Learn. Res., 2025)