mlsanityData to 7 Oct 2026

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees

arXiv 2408.04819 ↗First posted 9 Aug 2024cs.LG stat.MLcited by 1

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Abstract (arXiv)

Learning causal structure from sampled data is a fundamental problem with applications in various fields, including healthcare, machine learning and artificial intelligence. Traditional methods predominantly rely on observational data, but there exist limits regarding the identifiability of causal structures with only observational data. Interventional data, on the other hand, helps establish a cause-and-effect relationship by breaking the influence of confounding variables. It remains to date under-explored to develop a mathematical framework that seamlessly integrates both observational and interventional data in causal structure learning. Furthermore, existing studies often focus on centralized approaches, necessitating the transfer of entire datasets to a single server, which lead to considerable communication overhead and heightened risks to privacy. To tackle these challenges, we develop a bilevel polynomial optimization (Bloom) framework. Bloom not only provides a powerful mathematical modeling framework, underpinned by theoretical support, for causal structure discovery from both interventional and observational data, but also aspires to an efficient causal discovery algorithm with convergence and optimality guarantees. We further extend Bloom to a distributed setting to reduce the communication overhead and mitigate data privacy risks. It is seen through experiments on both synthetic and real-world datasets that Bloom markedly surpasses other leading learning algorithms.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 41 of its 81 references that are in the map. First posted on arXiv 9 Aug 2024.

Builds on

41 of 81 references.

References · 41

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29 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Survey on Federated Causal Discovery and Inferencecited by 0