Structure Learning with Bow-free Acyclic Path Diagrams
Year: 2015
Type: preprint
Abstract: We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG models that allow for certain hidden variables. We present a first method for this problem using a greedy score-based search algorithm. We also investigate some distributional equivalence properties of BAPs which are used in an algorithmic approach to compute (nearly) equivalent model structures, allowing to infer lower bounds of causal effects. The application of our method to some datasets reveals that BAP models can represent the data much better than DAG models in these cases.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2215484510