2023
DOI: 10.1609/aaai.v37i10.26445
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Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Abstract: We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature recover a graph through learning a causal order (c-order). We advocate for a novel order called removable order (r-order) as they are advantageous over c-orders for structure learning. This is because r-orders are the minimizers of an appropriately defined optim… Show more

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