On scoring Maximal Ancestral Graphs with the Max–Min Hill Climbing algorithm

Konstantinos Tsirlis, Vincenzo Lagani, Sofia Triantafillou, Ioannis Tsamardinos

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max–Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraint-based first phase that greatly reduces the space of structures to investigate. On a large scale experimentation we show that the proposed algorithm greatly improves on GSMAG in all comparisons, and over a set of known networks from the literature it compares positively against FCI and cFCI as well as competitively against GFCI, three well known constraint-based approaches for causal-network reconstruction in presence of latent confounders.
Original languageEnglish (US)
Pages (from-to)74-85
Number of pages12
JournalInternational Journal of Approximate Reasoning
StatePublished - Nov 1 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software
  • Applied Mathematics


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