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From Correlation to Causation: Formalizing Interpretable Machine
  Learning as a Statistical Process

From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process

11 July 2022
Lukas Klein
Mennatallah El-Assady
Paul F. Jäger
    CML
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Papers citing "From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process"

2 / 2 papers shown
Title
Model Interpretability through the Lens of Computational Complexity
Model Interpretability through the Lens of Computational Complexity
Pablo Barceló
Mikaël Monet
Jorge A. Pérez
Bernardo Subercaseaux
132
94
0
23 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
257
3,698
0
28 Feb 2017
1