Uncertainty in Artificial Intelligence
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Inferring deterministic causal relations
Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schoelkopf
Abstract:
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 (noise-free) 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 real-world data sets from different domains.
Keywords:
Pages: 143-150
PS Link:
PDF Link: /papers/10/p143-daniusis.pdf
BibTex:
@INPROCEEDINGS{Daniusis10,
AUTHOR = "Povilas Daniusis and Dominik Janzing and Joris Mooij and Jakob Zscheischler and Bastian Steudel and Kun Zhang and Bernhard Schoelkopf",
TITLE = "Inferring deterministic causal relations",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "143--150"
}


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