Non-linear Causal Inference using Gaussianity Measures

16 Sep 2014Daniel Hernández-LobatoPablo Morales-MombielaDavid Lopez-PazAlberto Suárez

We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of a linear fit in the anti-causal direction is closer to a Gaussian than the distribution of the residuals in the causal direction... (read more)

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