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Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers.

, , and . CoRR, (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 . CoRR, (2024)Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators., and . CoRR, (2021)Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies., , and . ICLR, OpenReview.net, (2022)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)Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects., , , , and . CoRR, (2022)Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time., , , , and . CoRR, (2021)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)