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Cautionary Tales on Synthetic Controls in Survival Analyses.

, , , and . CLeaR, volume 236 of Proceedings of Machine Learning Research, page 143-159. PMLR, (2024)

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SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data., , and . NeurIPS, page 26740-26753. (2021)Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability., , , and . NeurIPS, (2022)Defining Expertise: Applications to Treatment Effect Estimation., , , and . ICLR, OpenReview.net, (2024)HyperImpute: Generalized Iterative Imputation with Automatic Model Selection., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 9916-9937. PMLR, (2022)Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems., , , , , , and . PKDD/ECML Workshops (1), volume 1167 of Communications in Computer and Information Science, page 605-621. Springer, (2019)Cautionary Tales on Synthetic Controls in Survival Analyses., , , and . CLeaR, volume 236 of Proceedings of Machine Learning Research, page 143-159. PMLR, (2024)Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators., and . CoRR, (2021)In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation., and . ICML, volume 202 of Proceedings of Machine Learning Research, page 6623-6642. PMLR, (2023)Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies., , and . ICLR, OpenReview.net, (2022)Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation., , , and . NeurIPS Datasets and Benchmarks, (2021)