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From kernels to causal inference

From kernels to causal inference

This video was recorded at 25th Annual Conference on Neural Information Processing Systems (NIPS), Granada 2011. Kernel methods in machine learning have expanded from tricks to construct nonlinear algorithms to general tools to assay higher order statistics and properties of distributions. They find applications also in causal inference, an intriguing field that examines causal structures by testing their probabilistic footprints. However, the links between causal inference and modern machine learning go beyond this and the talk will outline some initial thoughts how problems like covariate shift adaptation and semi-supervised learning can benefit from the causal methodology.


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