| 2024 | ICLR | A Neural Framework for Generalized Causal Sensitivity Analysis. | Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar |
| 2024 | ICLR | Defining Expertise: Applications to Treatment Effect Estimation. | Alihan Hyk, Qiyao Wei, Alicia Curth, Mihaela van der Schaar |
| 2023 | AISTATS | Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. | Alicia Curth, Mihaela van der Schaar |
| 2023 | ICML | Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. | Alicia Curth, Alihan Hyk, Mihaela van der Schaar |
| 2023 | ICML | In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. | Alicia Curth, Mihaela van der Schaar |
| 2023 | ICML | Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. | Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar |
| 2022 | ICLR | Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. | Alex J. Chan, Alicia Curth, Mihaela van der Schaar |
| 2022 | ICML | HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. | Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar |
| 2021 | AISTATS | Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. | Alicia Curth, Mihaela van der Schaar |