Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Testing whether linear equations are causal: A free probability theory approach


Zscheischler,  Jakob
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Zscheischler, J., Janzing, D., & Zhang, K. (2011). Testing whether linear equations are causal: A free probability theory approach. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI). AUAI Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0018-1819-B
We propose a method that infers whether linear relations between two high-dimensional variables X and Y are due to a causal in- uence from X to Y or from Y to X. The earlier proposed so-called Trace Method is extended to the regime where the dimension of the observed variables exceeds the sample size. Based on previous work, we postulate conditions that characterize a causal relation between X and Y . Moreover, we describe a statistical test and argue that both causal directions are typically rejected if there is a common cause. A full theoretical analysis is presented for the deterministic case but our approach seems to be valid for the noisy case, too, for which we additionally present an approach based on a sparsity constraint. The discussed method yields promising results for both simulated and real world data.