| 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 | 42 | 2021 |
| Machine learning for clinical trials in the era of COVID-19 WR Zame, I Bica, C Shen, A Curth, HS Lee, S Bailey, J Weatherall, ... Statistics in Biopharmaceutical Research 12 (4), 506-517, 2020 | 42 | 2020 |
| 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 | 20 | 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 | 18 | 2021 |
| 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 | 15 | 2020 |
| 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 | 9 | 2022 |
| Estimating Structural Target Functions using Machine Learning and Influence Functions A Curth, AM Alaa, M van der Schaar arXiv preprint arXiv:2008.06461, 2020 | 9 | 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 | 7 | 2021 |
| 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 | 5 | 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 | 4 | 2022 |
| Inverse online learning: Understanding non-stationary and reactionary policies AJ Chan, A Curth, M van der Schaar arXiv preprint arXiv:2203.07338, 2022 | 3 | 2022 |
| 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 | 3 | 2021 |
| Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability J Crabbé*, A Curth*, I Bica*, M van der Schaar arXiv preprint arXiv:2206.08363, 2022 | 2 | 2022 |
| 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 | 2 | 2021 |
| Uncertainty and the US economy: an investigation of the economic effects of economic policy uncertainty shocks originating in the US and Europe A Curth Erasmus University Rotterdam, 2018 | 2 | 2018 |
| Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials A Curth, A Hüyük, M van der Schaar arXiv preprint arXiv:2208.05844, 2022 | 1* | 2022 |
| Identifying Good Arms Fast and With Confidence: Strategies and Empirical Insights A Curth, A Hüyük, M van der Schaar | | |
