# Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks

@article{Mooij2014DistinguishingCF, title={Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks}, author={Joris M. Mooij and J. Peters and Dominik Janzing and Jakob Zscheischler and Bernhard Sch{\"o}lkopf}, journal={ArXiv}, year={2014}, volume={abs/1412.3773} }

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias…

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