Detecting Low-Complexity Unobserved Causes
Abstract
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a "direct" causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a genetic marker that is correlated with a phenotype of interest, we want to detect whether this marker is causal or it only correlates with a causal one. Our method is based on the analysis of the location of the conditional distributions P(Y|x) in the simplex of all distributions of Y. We report encouraging results on semi-empirical data.
Cite
Text
Janzing et al. "Detecting Low-Complexity Unobserved Causes." Conference on Uncertainty in Artificial Intelligence, 2011.Markdown
[Janzing et al. "Detecting Low-Complexity Unobserved Causes." Conference on Uncertainty in Artificial Intelligence, 2011.](https://mlanthology.org/uai/2011/janzing2011uai-detecting/)BibTeX
@inproceedings{janzing2011uai-detecting,
title = {{Detecting Low-Complexity Unobserved Causes}},
author = {Janzing, Dominik and Sgouritsa, Eleni and Stegle, Oliver and Peters, Jonas and Schölkopf, Bernhard},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2011},
pages = {383-391},
url = {https://mlanthology.org/uai/2011/janzing2011uai-detecting/}
}