Publications

Find below a selection of my publications and preprints; a full list can be found on my google scholar profile.

(* indicates equal contribution)

Personal Favourite Publications 🤓

Jeffares, A.*, Curth, A.*, & van der Schaar, M. (2024). Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. In NeurIPS 2024. Arxiv Link.

Curth, A.*, Jeffares, A.*, & van der Schaar, M. (2023). A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. In NeurIPS 2023. Arxiv Link. NeurIPS Oral.

Curth, A., & van der Schaar, M. (2021). On inductive biases for heterogeneous treatment effect estimation. In Advances in Neural Information Processing Systems (NeurIPS), 34. Link NeurIPS Spotlight Paper.

Curth, A., Svensson, D., Weatherall, J., & van der Schaar, M. (2021). Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation. In Conference on neural information processing systems (NeurIPS) datasets and benchmarks track. Link

Selected Preprints

Curth, A. (2024). Classical Statistical (In-Sample) Intuitions Don’t Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs. Arxiv Link

Curth, A., Jeffares, A. & van der Schaar, M. (2024). Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. Arxiv Link

Further Selected Publications

Curth, A., Poon, H., Nori, A. & González, J. (2024). Cautionary Tales on Synthetic Controls in Survival Analyses. In Conference on Causal Learning and Reasoning (CLeaR). Arxiv Link

Curth, A., Peck, R., McKinney, E., Weatherall, J. & van der Schaar, M. (2024). Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities. In Clinical Pharmacology and Therapeutics. Paper Link

Curth, A., & van der Schaar, M. (2023). In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. In International Conference on Machine Learning (ICML). Arxiv Link

Curth, A., Hüyük, A., & van der Schaar, M. (2023). Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. In International Conference on Machine Learning (ICML). Arxiv Link (Also presented as Spotlight Paper at the Workshop on Adaptive Experimental Design and Active Learning in the Real World at ICML 2022.)

Vanderschueren*, T., Curth, A.*, Verbeke, W. & van der Schaar, M. (2023). Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. In International Conference on Machine Learning (ICML). Arxiv Link

Curth, A., & van der Schaar, M. (2023). Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR. Link

Crabbé, J.*, Curth, A.*, Bica, I.*, & van der Schaar, M. (2022). Benchmarking heterogeneous treatment effect models through the lens of interpretability. In Thirty-sixth conference on neural information processing systems (NeurIPS) datasets and benchmarks track. Link

Curth, A*, Lee, C.*, & van der Schaar, M. (2021). SurvITE: learning heterogeneous treatment effects from time-to-event data. In Advances in Neural Information Processing Systems (NeurIPS), 34. Link

Curth, A., & van der Schaar, M. (2021). Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms. In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR. Link

Curth, A., Alaa, A. M., & van der Schaar, M. (2020). Estimating structural target functions using machine learning and influence functions. MSc Dissertation. Arxiv Link MSc Dissertation.

Curth, A., Thoral, P., van den Wildenberg, W., Bijlstra, P., de Bruin, D., Elbers, P., & Fornasa, M. (2019). Transferring clinical prediction models across hospitals and electronic health record systems. In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I (pp. 605-621). Springer International Publishing. Link