arXiv · 1807.03419
On Causal Discovery with Equal Variance Assumption
Abstract
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances. We show that this fact is implied by an ordering among (conditional) variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Wenyu Chen, Mathias Drton, Y. Samuel Wang. 2019-03-07. On Causal Discovery with Equal Variance Assumption. https://doi.org/10.1093/biomet%2Fasz049
Cite the original work for its findings. Save a collection to share your selection of sources.