Weakening faithfulness: some heuristic causal discovery algorithms

Zhalama*, Jiji Zhang, Wolfgang Mayer

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


We examine the performance of some standard causal discovery algorithms, both constraint-based and score-based, from the perspective of how robust they are against (almost) failures of the Causal Faithfulness Assumption. For this purpose, we make only the so-called Triangle-Faithfulness assumption, which is a fairly weak consequence of the Faithfulness assumption, and otherwise allows unfaithful distributions. In particular, we allow violations of Adjacency-Faithfulness and Orientation-Faithfulness. We show that the (conservative) PC algorithm, a representative constraint-based method, can be made more robust against unfaithfulness by incorporating elements of the GES algorithm, a representative score-based method; similarly, the GES algorithm can be made less error-prone by incorporating elements of the conservative PC algorithm. As our simulations demonstrate, the increased robustness seems to matter even when faithfulness is not exactly violated, for with only finite sample, distributions that are not exactly unfaithful may be sufficiently close to being unfaithful to make trouble.
Original languageEnglish
Pages (from-to)93–104
Number of pages12
JournalInternational Journal of Data Science and Analytics
Issue number2
Publication statusPublished - Mar 2017

User-Defined Keywords

  • Causal discovery
  • Faithfulness
  • PC
  • GES


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