
Analysis of CauseEffect Inference via Regression Errors
We address the problem of inferring the causal relation between two vari...
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Telling cause from effect based on highdimensional observations
We describe a method for inferring linear causal relations among multid...
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Testing whether linear equations are causal: A free probability theory approach
We propose a method that infers whether linear relations between two hig...
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Causal Inference on Discrete Data using Additive Noise Models
Inferring the causal structure of a set of random variables from a finit...
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Error Asymmetry in Causal and Anticausal Regression
It is generally difficult to make any statements about the expected pred...
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A Kernel Embeddingbased Approach for Nonstationary Causal Model Inference
Although nonstationary data are more common in the real world, most exis...
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Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to ...
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Inferring deterministic causal relations
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noisefree) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various realworld data sets from different domains.
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