Statistical Learning: Causal-oriented and Robust
Peter Bühlmann, Department of Mathematics, ETH Zürich
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 and external validity, with wide-ranging prospects for various applications.
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