Related papers: Structure Learning for Cyclic Linear Causal Models

Structure Learning for Cyclic Linear Causal Models

URL: http://arxiv.org/abs/2006.05978v2
Date: Wed, 19 Aug 2020 20:52:25 GMT
Title: Structure Learning for Cyclic Linear Causal Models
Authors: Carlos Am\'endola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu
Abstract summary: We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feedback loops and effects of latent confounders.
Score: 5.567377163246147
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feedback loops and effects of latent confounders. Generalizing related work on bow-free acyclic graphs, we assume that the underlying graph is simple. This entails that any two observed variables can be related through at most one direct causal effect and that (confounding-induced) correlation between error terms in structural equations occurs only in absence of direct causal effects. We show that, despite new subtleties in the cyclic case, the considered simple cyclic models are of expected dimension and that a previously considered criterion for distributional equivalence of bow-free acyclic graphs has an analogue in the cyclic case. Our result on model dimension justifies in particular score-based methods for structure learning of linear Gaussian mixed graph models, which we implement via greedy search.

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