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Causality for Machine Learning
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Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.
Forward citations
Cited by 2 Pith papers
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Mitigating Spurious Correlations with Causal Logit Perturbation
CLP trains a perturbation network via meta-learning on causally augmented metadata to adjust classifier logits per sample, improving accuracy on four spurious-correlation benchmarks.
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Learning Causality for Modern Machine Learning
A thesis compiling six papers that use causal invariance to improve graph neural networks' out-of-distribution generalization, interpretability, and robustness.
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