NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:7042
Title:Triad Constraints for Learning Causal Structure of Latent Variables

In this paper, the authors propose a novel kind of constraint they call the 'triad constraint' (presumably by analogy with tetrad constraints), which allow causal discovery to determine structure among latent variables, under certain (strong) parametric assumptions. The reviewers appreciated this paper as providing a novel contribution to the causal discovery literature.