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Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey

arXiv 2608.11156 ↗First posted 11 Aug 2026stat.ML cs.LGcited by 0

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Abstract (arXiv)

Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions. This survey reviews CI testing with emphasis on assumptions, robustness, and scalability in high-dimensional and mixed-type settings common in biomedical domains. The survey organizes widely used CI methods into six families: partial-correlation, contingency-table, regression, nearest-neighbor, kernel, and machine-learning-based. Special emphasis is provided on the robustness layers that address the limitations of these families. For each family, the survey examines when CI decisions reflect the data-generating distribution and when they fail. By this, we link test-level properties, including power decay with conditioning set size and asymmetric type I/II error consequences, to graph-level errors in skeleton recovery and v-structure orientation. The survey also compares adoption across major R and Python libraries and summarizes open challenges, including mixed-type CI testing without discretization, small-sample error control, and strategies for improving scalability of CI-testing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 118 references that are in the map. First posted on arXiv 11 Aug 2026.

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References · 29

  1. 2017Counterfactual Fairnesscited by 804
  2. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
  3. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  4. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  5. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  6. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  7. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  8. 2023Causal-learn: Causal Discovery in Pythoncited by 109
  9. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
  10. 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
  11. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  12. 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
17 more
  1. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  2. 2024Multi-Agent Causal Discovery Using Large Language Modelscited by 26
  3. 2023pgmpy: A Python Toolkit for Bayesian Networkscited by 23
  4. 2021The Weighted Generalised Covariance Measurecited by 13
  5. 2015A hybrid algorithm for Bayesian network structure learning with application to multi-label learningcited by 12
  6. 2018cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPUcited by 12
  7. 2016Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsetscited by 10
  8. 2024Conditional Diffusion Models Based Conditional Independence Testingcited by 9
  9. 2016Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Valuescited by 8
  10. 2015Learning Discrete Bayesian Networks from Continuous Datacited by 6
  11. 2023Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discoverycited by 6
  12. 2020Directed Acyclic Graphs and causal thinking in clinical risk prediction modelingcited by 5
  13. 2025Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Datacited by 5
  14. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  15. 2024A Conditional Independence Test in the Presence of Discretizationcited by 3
  16. 2017Mixed Graphical Models for Causal Analysis of Multi-modal Variablescited by 1
  17. 2023Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluationcited by 1

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