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perspectiva aiqbenchMachine learning

This research introduces a novel branch-and-cut algorithm for learning maximally ancestral graphs within mixed-graph settings to address causal inference in the presence of confounders. By formulating the problem as a mixed-integer quadratic program, the method achieves superior sample efficiency and reconstruction quality compared to existing state-of-the-art approaches. This advancement directly enhances machine learning capabilities for modeling complex causal structures where traditional directed acyclic graphs are insufficient.

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ExMAG: Learning of Maximally Ancestral Graphs

arXiv21 iulie 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2503.08245v4 Announce Type: replace Abstract: In mixed graphs, there are both directed and bidirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of considerable interest in causal learning in the presence of confounders. There, directed edges represent a clear direction of causality, while bidirected edges represent confounding. We propose a branch-and-cut algorithm for learning maximally ancestral graphs using a formulation as a mixed-integer quadratic program. Empirically, our method achieves comparable or improved reconstruction quality while requiring an order of magnitude fewer samples than state-of-the-art approaches.

Abstract:In mixed graphs, there are both directed and bidirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of considerable interest in causal learning in the presence of confounders. There, directed edges represent a clear direction of causality, while bidirected edges represent confounding. We propose a branch-and-cut algorithm for learning maximally ancestral graphs using a formulation as a mixed-integer quadratic program. Empirically, our method achieves comparable or improved reconstruction quality while requiring an order of magnitude fewer samples than state-of-the-art approaches.

Machine Learning (cs.LG)

arXiv:2503.08245 [cs.LG]

(or arXiv:2503.08245v4 [cs.LG] for this version)

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