mlsanityData to 7 Oct 2026

Do we become wiser with time? On causal equivalence with tiered background knowledge

arXiv 2306.01638 ↗First posted 2 Jun 2023stat.ML cs.LG math.STcited by 9

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Abstract (arXiv)

Equivalence classes of DAGs (represented by CPDAGs) may be too large to provide useful causal information. Here, we address incorporating tiered background knowledge yielding restricted equivalence classes represented by 'tiered MPDAGs'. Tiered knowledge leads to considerable gains in informativeness and computational efficiency: We show that construction of tiered MPDAGs only requires application of Meek's 1st rule, and that tiered MPDAGs (unlike general MPDAGs) are chain graphs with chordal components. This entails simplifications e.g. of determining valid adjustment sets for causal effect estimation. Further, we characterise when one tiered ordering is more informative than another, providing insights into useful aspects of background knowledge.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 5 of its 33 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

5 of 33 references.

References · 5

  1. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  2. 2014Estimating the effect of joint interventions from observational data in sparse high-dimensional settingscited by 20
  3. 2020Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGscited by 14
  4. 2020Minimal enumeration of all possible total effects in a Markov equivalence classcited by 12
  5. 2022On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inferencecited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 4.

9 papers.

  1. 2024Local Causal Discovery with Background Knowledgecited by 5
  2. 2024Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structurecited by 3
  3. 2025Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasetscited by 2
  4. 2026Coarsening Causal DAG Modelscited by 2
  5. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 1
  6. 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributionscited by 0
  7. 2026Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variablescited by 0
  8. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  9. 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0