| Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms A Curth, M Van der Schaar International Conference on Artificial Intelligence and Statistics, 1810-1818, 2021 | 166 | 2021 |
| Causal machine learning for predicting treatment outcomes S Feuerriegel, D Frauen, V Melnychuk, J Schweisthal, K Hess, A Curth, ... Nature Medicine 30 (4), 958-968, 2024 | 89 | 2024 |
| On Inductive Biases for Heterogeneous Treatment Effect Estimation A Curth, M van der Schaar Proceedings of the 35th Conference on Neural Information Processing Systems …, 2021 | 81 | 2021 |
| Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation A Curth, D Svensson, J Weatherall, M van der Schaar Proceedings of the Neural Information Processing Systems Track on Datasets …, 2021 | 80 | 2021 |
| Hyperimpute: Generalized iterative imputation with automatic model selection D Jarrett, BC Cebere, T Liu, A Curth, M van der Schaar International Conference on Machine Learning, 9916-9937, 2022 | 75 | 2022 |
| Machine learning for clinical trials in the era of COVID-19 WR Zame, I Bica, C Shen, A Curth, HS Lee, S Bailey, M van der Schaar Statistics in Biopharmaceutical Research 12 (4), 506-517, 2020 | 75 | 2020 |
| SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data A Curth*, C Lee*, M van der Schaar Proceedings of the 35th Conference on Neural Information Processing Systems …, 2021 | 42 | 2021 |
| In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation A Curth, M van der Schaar International Conference on Machine Learning (ICML) 2023, 2023 | 33 | 2023 |
| Combining observational and randomized data for estimating heterogeneous treatment effects T Hatt, J Berrevoets, A Curth, S Feuerriegel, M van der Schaar arXiv preprint arXiv:2202.12891, 2022 | 30 | 2022 |
| Transferring clinical prediction models across hospitals and electronic health record systems A Curth, P Thoral, W van den Wildenberg, P Bijlstra, D de Bruin, P Elbers, ... Machine Learning and Knowledge Discovery in Databases: International …, 2020 | 28 | 2020 |
| A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning A Curth*, A Jeffares*, M van der Schaar Proceedings of the 37th Conference on Neural Information Processing Systems …, 2023 | 24 | 2023 |
| Estimating Structural Target Functions using Machine Learning and Influence Functions A Curth, AM Alaa, M van der Schaar MSc Dissertation, University of Oxford, 2020 | 24* | 2020 |
| Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability J Crabbé*, A Curth*, I Bica*, M van der Schaar Proceedings of the Neural Information Processing Systems Track on Datasets …, 2022 | 20 | 2022 |
| Using machine learning to individualize treatment effect estimation: Challenges and opportunities A Curth, RW Peck, E McKinney, J Weatherall, M van Der Schaar Clinical Pharmacology & Therapeutics 115 (4), 710-719, 2024 | 19 | 2024 |
| Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time T Vanderschueren*, A Curth*, W Verbeke, M van der Schaar International Conference on Machine Learning (ICML) 2023, 2023 | 19 | 2023 |
| Estimating multi-cause treatment effects via single-cause perturbation Z Qian, A Curth, M van der Schaar Advances in Neural Information Processing Systems 34, 23754-23767, 2021 | 18 | 2021 |
| Disentangled counterfactual recurrent networks for treatment effect inference over time J Berrevoets, A Curth, I Bica, E McKinney, M van der Schaar arXiv preprint arXiv:2112.03811, 2021 | 15 | 2021 |
| Why do random forests work? Understanding tree ensembles as self-regularizing adaptive smoothers A Curth, A Jeffares, M van der Schaar arXiv preprint arXiv:2402.01502, 2024 | 10 | 2024 |
| A neural framework for generalized causal sensitivity analysis D Frauen, F Imrie, A Curth, V Melnychuk, S Feuerriegel, M van der Schaar arXiv preprint arXiv:2311.16026, 2023 | 9 | 2023 |
| Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators A Curth, M van der Schaar Workshop on the Neglected Assumptions in Causal Inference, ICML 2021, 2021 | 8 | 2021 |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing InstituteAdresse e-mail validée de ee.ucla.edu