Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems. - Alicia Curth; Patrick Thoral; Wilco van den Wildenberg; Peter Bijlstra; Daan P. de Bruin; Paul W. G. Elbers; Mattia Fornasa (PKDD/ECML Workshops (1), 2019)

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. - Alicia Curth; Mihaela van der Schaar (AISTATS, 2021)

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. - Alicia Curth; Changhee Lee; Mihaela van der Schaar (NeurIPS, 2021)

On Inductive Biases for Heterogeneous Treatment Effect Estimation. - Alicia Curth; Mihaela van der Schaar (NeurIPS, 2021)

Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation. - Alicia Curth; David Svensson; James Weatherall; Mihaela van der Schaar (NeurIPS Datasets and Benchmarks, 2021)

Estimating Multi-cause Treatment Effects via Single-cause Perturbation. - Zhaozhi Qian; Alicia Curth; Mihaela van der Schaar (NeurIPS, 2021)

Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators. - Alicia Curth; Mihaela van der Schaar (CoRR, 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)

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. - Alex J. Chan; Alicia Curth; Mihaela van der Schaar (ICLR, 2022)

HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. - Daniel Jarrett; Bogdan Cebere; Tennison Liu; Alicia Curth; Mihaela van der Schaar (ICML, 2022)

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. - Jonathan Crabbé; Alicia Curth; Ioana Bica; Mihaela van der Schaar (NeurIPS, 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)

Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. - Alicia Curth; Alihan Hüyük; Mihaela van der Schaar (CoRR, 2022)

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. - Alicia Curth; Mihaela van der Schaar (AISTATS, 2023)

Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. - Alicia Curth; Alihan Hüyük; Mihaela van der Schaar (ICML, 2023)

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 (ICML, 2023)

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. - Toon Vanderschueren; Alicia Curth; Wouter Verbeke; Mihaela van der Schaar (ICML, 2023)

A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. - Alicia Curth; Alan Jeffares; Mihaela van der Schaar (NeurIPS, 2023)

Cautionary Tales on Synthetic Controls in Survival Analyses. - Alicia Curth; Hoifung Poon; Aditya V. Nori; Javier González 002 (CLeaR, 2024)

A Neural Framework for Generalized Causal Sensitivity Analysis. - Dennis Frauen; Fergus Imrie; Alicia Curth; Valentyn Melnychuk; Stefan Feuerriegel; Mihaela van der Schaar (ICLR, 2024)

Defining Expertise: Applications to Treatment Effect Estimation. - Alihan Hüyük; Qiyao Wei; Alicia Curth; Mihaela van der Schaar (ICLR, 2024)

Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. - Alan Jeffares; Alicia Curth; Mihaela van der Schaar (NeurIPS, 2024)

Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. - Alicia Curth; Alan Jeffares; Mihaela van der Schaar (CoRR, 2024)

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

Causal machine learning for predicting treatment outcomes. - Stefan Feuerriegel; Dennis Frauen; Valentyn Melnychuk; Jonas Schweisthal; Konstantin Hess; Alicia Curth; Stefan Bauer; Niki Kilbertus; Isaac S. Kohane; Mihaela van der Schaar (CoRR, 2024)