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As predictive models -- e.g., from machine learning -- give likely outcomes, they may be used to reason on the effect of an intervention, a causal-inference task.
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Peter Austin and Elizabeth Stuart · 2017
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“Use of Propensity Score Methodology in Contemporary High-Impact Surgical Literature” Publisher: Elsevier
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Aris Perperoglou, Willi Sauerbrei, Michal Abrahamowicz and Matthias Schmid · 2019
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MA Hernán and JM Robins · 2020
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Andrew Jesson, Sören Mindermann, Uri Shalit and Yarin Gal · 2020
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Edward Kennedy · 2020
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Yuta Saito and Shota Yasui · 2020
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Xavier Bouthillier et al · 2021
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Alexander D’Amour et al · 2021
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Zijun Gao, Trevor Hastie and Robert Tibshirani · 2021
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“Methods of Public Health Research — Strengthening Causal Inference from Observational Data”
Miguel. Hernán · 2021
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“A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint”
Jeroen Hoogland et al · 2021
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“Revisiting the Calibration of Modern Neural Networks”
Matthias Minderer et al · 2021
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“Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms”
Ashley Naimi, Alan Mishler and Edward Kennedy · 2021
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“External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning”
Nicolas Loiseau et al · 2022
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“Beyond calibration: estimating the grouping loss of modern neural networks”
Alexandre Perez-Lebel, Marine Morvan and Gaël Varoquaux · 2022
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“Evaluating machine learning models and their diagnostic value”
Gaël Varoquaux and Olivier Colliot · 2022
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“Risk ratio, odds ratio, risk difference… Which causal measure is easier to generalize?”
Bénédicte Colnet, Julie Josse, Gaël Varoquaux and Erwan Scornet · 2023
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Ashley Naimi and Brian Whitcomb · 2023
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Tony Blakely et al · 2064
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