mlsanityData to 7 Oct 2026

Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure

arXiv 2406.19503 ↗First posted 27 Jun 2024stat.ME stat.MLcited by 3

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Abstract (arXiv)

Methods of causal discovery aim to identify causal structures in a data driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We present an algorithm that efficiently exploits temporal structure, so-called tiered background knowledge, for estimating causal structures. Tiered background knowledge is readily available from, e.g., cohort or registry data. When used efficiently it renders the algorithm more robust to statistical errors and ultimately increases accuracy in finite samples. We describe the algorithm and illustrate how it proceeds. Moreover, we offer formal proofs as well as examples of desirable properties of the algorithm, which we demonstrate empirically in an extensive simulation study. To illustrate its usefulness in practice, we apply the algorithm to data from a children's cohort study investigating the interplay of diet, physical activity and other lifestyle factors for health outcomes.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 57 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

9 of 57 references.

References · 9

  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  3. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  4. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  5. 2022Valid Inference After Causal Discoverycited by 10
  6. 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
  7. 2024Post-selection inference for causal effects after causal discoverycited by 7
  8. 2024A cautious approach to constraint-based causal model selectioncited by 6
  9. 2023Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variancescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithmscited by 5
  2. 2025Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasetscited by 2
  3. 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0