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arXiv · 2305.19884

Positivity in Linear Gaussian Structural Equation Models

Asad Lodhia·Jan-Christian Hütter·Caroline Uhler·Piotr Zwiernik
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Abstract

We study a notion of positivity of Gaussian directed acyclic graphical models corresponding to a non-negativity constraint on the coefficients of the associated structural equation model. We prove that this constraint is equivalent to the distribution being conditionally increasing in sequence (CIS), a well-known subclass of positively associated random variables. These distributions require knowledge of a permutation, a CIS ordering, of the nodes for which the constraint of non-negativity holds. We provide an algorithm and prove in the noise-less setting that a CIS ordering can be recovered when it exists. We extend this result to the noisy setting and provide assumptions for recovering the CIS orderings. In addition, we provide a characterization of Markov equivalence for CIS DAG models. Further, we show that when a CIS ordering is known, the corresponding class of Gaussians lies in a family of distributions in which maximum likelihood estimation is a convex problem.

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Asad Lodhia, Jan-Christian Hütter, Caroline Uhler, Piotr Zwiernik. 2023-05-31. Positivity in Linear Gaussian Structural Equation Models. https://arxiv.org/abs/2305.19884

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