ExMAG: Learning of Maximally Ancestral Graphs

2025/03/11 by Petr Ryšavý, Ryšavý, Petr, Pavel Rytíř +7 · 1 citation
Computer Science · #Bayesian Modeling and Causal Inference #Advanced Graph Neural Networks #Machine Learning and Algorithms

paper · pdf · doi:10.48550/arxiv.2503.08245

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.

Citations

• Doubly debiased lasso: High-dimensional inference under hidden confounding
• Factor modeling for high-dimensional time series: Inference for the number of factors
• The central role of the propensity score in observational studies for causal effects
• A tree-search algorithm for mixed integer programming problems

Cited by

• Differentiable Cyclic Causal Discovery Under Unmeasured Confounders

Related

• σ-Maximal Ancestral Graphs
• Towards Complete Causal Explanation with Expert Knowledge
• An efficient search-and-score algorithm for ancestral graphs using multivariate information scores
• Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
• Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs