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Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability


Jun 16, 2022
Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar


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HyperImpute: Generalized Iterative Imputation with Automatic Model Selection


Jun 15, 2022
Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar

* In Proc. 39th International Conference on Machine Learning (ICML 2022) 

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Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies


Mar 14, 2022
Alex J. Chan, Alicia Curth, Mihaela van der Schaar


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Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects


Feb 25, 2022
Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar


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Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time


Dec 07, 2021
Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin McKinney, Mihaela van der Schaar


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SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data


Oct 26, 2021
Alicia Curth, Changhee Lee, Mihaela van der Schaar

* To Appear in the Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) 

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Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators


Jul 28, 2021
Alicia Curth, Mihaela van der Schaar

* Workshop on the Neglected Assumptions in Causal Inference at the International Conference on Machine Learning (ICML), 2021 

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On Inductive Biases for Heterogeneous Treatment Effect Estimation


Jun 07, 2021
Alicia Curth, Mihaela van der Schaar


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Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms


Feb 25, 2021
Alicia Curth, Mihaela van der Schaar

* To appear in the Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 

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