Algebraic Equivalence of Linear Structural Equation Models

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Authors Joris M. Mooij, Thijs van Ommen
Journal/Conference Name Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017
Paper Category
Paper Abstract Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems. For causal discovery, two of these problems are especially important the enumeration of the constraints imposed by a model, and deciding whether two graphs define the same statistical model. We show how the half-trek criterion can be used to make progress in both of these problems. We apply our theoretical results to a small-scale model selection problem, and find that taking the additional algebraic constraints into account may lead to significant improvements in model selection accuracy.
Date of publication 2018
Code Programming Language Python

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