Cause Effect Pairs in Machine Learning
Berna Bakir Batu (Hrsg.), Alexander Statnikov (Hrsg.), Isabelle Guyon (Hrsg.)
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Springer International Publishing
Naturwissenschaften, Medizin, Informatik, Technik / Informatik
Beschreibung
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.
Kundenbewertungen
causal mechanisms, causality in machine learning, cause-effect pairs, Causality, causal graphs, causal inference, large scale design, causal direction, causal structure learning