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Alicia Curth

Causal machine learning for predicting treatment outcomes

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Oct 11, 2024
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Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs

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Sep 27, 2024
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Defining Expertise: Applications to Treatment Effect Estimation

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Mar 01, 2024
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Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers

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Feb 02, 2024
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A Neural Framework for Generalized Causal Sensitivity Analysis

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Nov 27, 2023
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A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning

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Oct 29, 2023
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Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time

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Jun 07, 2023
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Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data

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Feb 23, 2023
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In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation

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Feb 06, 2023
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Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials

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Aug 11, 2022
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