Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets
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Abstract (arXiv)
In this paper we consider the use of tiered background knowledge within constraint based causal discovery. Our focus is on settings relaxing causal sufficiency, i.e. allowing for latent variables which may arise because relevant information could not be measured at all, or not jointly, as in the case of multiple overlapping datasets. We first present novel insights into the properties of the 'tiered FCI' (tFCI) algorithm. Building on this, we introduce a new extension of the IOD (integrating overlapping datasets) algorithm incorporating tiered background knowledge, the 'tiered IOD' (tIOD) algorithm. We show that under full usage of the tiered background knowledge tFCI and tIOD are sound, while simple versions of the tIOD and tFCI are sound and complete. We further show that the tIOD algorithm can often be expected to be considerably more efficient and informative than the IOD algorithm even beyond the obvious restriction of the Markov equivalence classes. We provide a formal result on the conditions for this gain in efficiency and informativeness. Our results are accompanied by a series of examples illustrating the exact role and usefulness of tiered background knowledge.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 39 references that are in the map. First posted on arXiv 27 Mar 2025.
Builds on
8 of 39 references.
References · 8
- 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
- 2013Learning Sparse Causal Models is not NP-hardcited by 49
- 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
- 2019Integrating overlapping datasets using bivariate causal discoverycited by 12
- 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
- 2024Towards Complete Causal Explanation with Expert Knowledgecited by 7
- 2024Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structurecited by 3
- 2021Multiple imputation and test-wise deletion for causal discovery with incomplete cohort datacited by 2
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Citing papers per year: 2026: 2.
2 papers.
- 2026Multi-Level Causal Embeddingscited by 0
- 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0
- 2026Integrating Background Knowledge for Scalable Causal Discovery11 Jul 2026
- 2026Multi-Level Causal Embeddings25 Feb 2026