Statistical Learning: Causal-oriented and Robust

author: Peter Bühlmann, ETH Zurich
published: July 6, 2021,   recorded: July 2021,   views: 12

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Reliable, robust and interpretable machine learning is a big emerging theme in data science and artificial intelligence, complementing the development of pure black box prediction algorithms. Looking through the lens of statistical causality and exploiting a probabilistic invariance property opens up new paths and opportunities for enhanced robustness, with wide-ranging prospects for various applications.

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