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- W2981517195 abstract "Telling cause from effect from observations of just two variables has attracted increasing interest since more than one decade. On the one hand, it defines a nice binary classification problem for which it is easy to define a success rate, in contrast to more general causal inference tasks where no straightforward performance criteria exist. On the other hand, it fascinates researchers because solving this elementary task implies statistical asymmetries between cause and effect that were previously unknown. Discussing some real-world and toy examples, I argue that humans seem to have some intuition about these asymmetries, but I also argue that some straightforward ideas to distinguish between cause and effect are flawed. The discussion on the origin of the true asymmetries relates machine learning, philosophy and physics. For instance, the postulate that Pcause and Peffect|cause contain no information about each other (while Peffect and Pcause|effect may satisfy some ‘non-generic’ relations), is relevant for semi-supervised learning on the one hand, but is also related to the thermodynamic arrow of time on the other hand." @default.
- W2981517195 created "2019-11-01" @default.
- W2981517195 creator A5013941262 @default.
- W2981517195 date "2019-01-01" @default.
- W2981517195 modified "2023-09-26" @default.
- W2981517195 title "The Cause-Effect Problem: Motivation, Ideas, and Popular Misconceptions" @default.
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- W2981517195 doi "https://doi.org/10.1007/978-3-030-21810-2_1" @default.
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