Learning Sparse Causal Models is not NP-hard
Abstract
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N^{2(k+2)} independence tests, even when latent variables and selection bias may be present. We present a modification of the well-known FCI algorithm that implements the method for an independence oracle, and suggest improvements for sample/real-world data versions. It does not contradict any known hardness results, and does not solve an NP-hard problem: it just proves that sparse causal discovery is perhaps more complicated, but not as hard as learning minimal Bayesian networks.
Cite
@article{arxiv.1309.6824,
title = {Learning Sparse Causal Models is not NP-hard},
author = {Tom Claassen and Joris Mooij and Tom Heskes},
journal= {arXiv preprint arXiv:1309.6824},
year = {2013}
}
Comments
Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)