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

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SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data., , и . NeurIPS, стр. 26740-26753. (2021)Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability., , , и . NeurIPS, (2022)Defining Expertise: Applications to Treatment Effect Estimation., , , и . CoRR, (2024)Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators., и . CoRR, (2021)In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation., и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 6623-6642. PMLR, (2023)Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies., , и . ICLR, OpenReview.net, (2022)Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers., , и . CoRR, (2024)Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data., и . AISTATS, том 206 из Proceedings of Machine Learning Research, стр. 7961-7980. PMLR, (2023)Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms., и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 1810-1818. PMLR, (2021)Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions., , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 6603-6622. PMLR, (2023)