Alicia Curth

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 34

Top Topics

Nonparametric Estimation

Statistical Learning

Top Venues

Publications

Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
A Neural Framework for Generalized Causal Sensitivity Analysis. ICLR (2024)
Alicia Curth, Alan Jeffares, Mihaela van der Schaar
Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. CoRR (2024)
Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González
Cautionary Tales on Synthetic Controls in Survival Analyses. CLeaR (2024)
Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar
Defining Expertise: Applications to Treatment Effect Estimation. ICLR (2024)
Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar
Defining Expertise: Applications to Treatment Effect Estimation. CoRR (2024)
Alicia Curth, Alan Jeffares, Mihaela van der Schaar
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. CoRR (2023)
Alicia Curth, Mihaela van der Schaar
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. AISTATS (2023)
Alicia Curth, Mihaela van der Schaar
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. ICML (2023)
Alicia Curth, Alan Jeffares, Mihaela van der Schaar
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. NeurIPS (2023)
Alicia Curth, Mihaela van der Schaar
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. CoRR (2023)
Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. ICML (2023)
Alicia Curth, Alihan Hüyük, Mihaela van der Schaar
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. ICML (2023)
Alicia Curth, Mihaela van der Schaar
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. CoRR (2023)
Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. CoRR (2023)
Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
A Neural Framework for Generalized Causal Sensitivity Analysis. CoRR (2023)
Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. CoRR (2022)
Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS (2022)
Alicia Curth, Alihan Hüyük, Mihaela van der Schaar
Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. CoRR (2022)
Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. ICML (2022)
Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. CoRR (2022)
Alex J. Chan, Alicia Curth, Mihaela van der Schaar
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. CoRR (2022)
Alex J. Chan, Alicia Curth, Mihaela van der Schaar
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. ICLR (2022)
Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. CoRR (2022)
Alicia Curth, Changhee Lee, Mihaela van der Schaar
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. CoRR (2021)
Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin F. McKinney, Mihaela van der Schaar
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time. CoRR (2021)
Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar
Estimating Multi-cause Treatment Effects via Single-cause Perturbation. NeurIPS (2021)
Alicia Curth, Mihaela van der Schaar
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators. CoRR (2021)
Alicia Curth, Mihaela van der Schaar
On Inductive Biases for Heterogeneous Treatment Effect Estimation. NeurIPS (2021)
Alicia Curth, Mihaela van der Schaar
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. CoRR (2021)
Alicia Curth, Changhee Lee, Mihaela van der Schaar
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. NeurIPS (2021)
Alicia Curth, Mihaela van der Schaar
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. AISTATS (2021)
Alicia Curth, Mihaela van der Schaar
On Inductive Biases for Heterogeneous Treatment Effect Estimation. CoRR (2021)
Alicia Curth, David Svensson, James Weatherall, Mihaela van der Schaar
Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation. NeurIPS Datasets and Benchmarks (2021)
Alicia Curth, Patrick Thoral, Wilco van den Wildenberg, Peter Bijlstra, Daan de Bruin, Paul W. G. Elbers, Mattia Fornasa
Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems. PKDD/ECML Workshops (1) (2019)