mlsanityData to 7 Oct 2026

How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors

arXiv 2502.14719 ↗First posted 20 Feb 2025stat.ML cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Causal discovery methods based on the PC algorithm are proven to be sound if all structural assumptions are fulfilled and all conditional independence tests are correct. This idealized setting is rarely given in real data. In this work, we first analyze how local errors can propagate throughout the output graph of a PC-based method, highlighting how consequential seemingly innocuous errors can become. Next, we introduce coherency scores to find assumption violations and small sample errors in the absence of a ground truth. These scores do not require statistical tests beyond those already executed by the causal discovery algorithm. Errors detected by our approach extend the set of errors that can be detected by comparable existing methods. We place our computationally cheap global error detection and quantification scores as a bridge between computationally expensive global answer-set-programming-based methods and less expensive local error detection methods. The scores are analyzed on simulated and real-world datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 20 Feb 2025.

Builds on

15 of 46 references.

References · 15

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
  3. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  4. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  5. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  6. 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
  7. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  8. 2024Adjustment Identification Distance: A gadjid for Causal Structure Learningcited by 16
  9. 2016Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-valuecited by 11
  10. 2024Do causal predictors generalize better to new domains?cited by 11
  11. 2022Quantitative probing: Validating causal models using quantitative domain knowledgecited by 6
  12. 2024A cautious approach to constraint-based causal model selectioncited by 6
3 more
  1. 2025What is causal about causal models and representations?cited by 6
  2. 2024Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithmscited by 5
  3. 2024Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truthcited by 1

Built on by

Not cited yet.