mlsanityData to 7 Oct 2026

Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

arXiv 1702.03877 ↗First posted 13 Feb 2017stat.ME stat.MLcited by 76

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Abstract (arXiv)

Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test scales cubicly with sample size. We therefore devise two relaxations called the Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT) which both approximate KCIT by utilizing random Fourier features. In practice, both of the proposed tests scale linearly with sample size and return accurate p-values much faster than KCIT in the large sample size context. CCD algorithms run with RCIT or RCoT also return graphs at least as accurate as the same algorithms run with KCIT but with large reductions in run time.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 9; 2023: 14; 2024: 15; 2025: 13; 2026: 14.

76 papers.

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  1. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  2. 2021OOD-GNN: Out-of-Distribution Generalized Graph Neural Networkcited by 78
  3. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  4. 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
  5. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  6. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 29
  7. 2021A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalizationcited by 18
  8. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  9. 2024Federated Causal Discovery from Heterogeneous Datacited by 15
  10. 2024Practical Kernel Tests of Conditional Independencecited by 13
  11. 2021An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddingscited by 12
  12. 2021Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledgecited by 10
38 more
  1. 2021Obtaining Causal Information by Merging Datasets with MAXENTcited by 9
  2. 2022Conditional Independence Testing via Latent Representation Learningcited by 9
  3. 2021Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regressioncited by 8
  4. 2023Conditional independence testing under misspecified inductive biasescited by 7
  5. 2021Semiparametric Bayesian Networkscited by 6
  6. 2023Nearest-Neighbor Sampling Based Conditional Independence Testingcited by 5
  7. 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacencycited by 5
  8. 2024Causal Representation Learning in Temporal Data via Single-Parent Decodingcited by 5
  9. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  10. 2021Marginalizable Density Modelscited by 4
  11. 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledgecited by 4
  12. 2025The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representationscited by 4
  13. 2025Differentiable Constraint-Based Causal Discoverycited by 4
  14. 2021A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustmentcited by 3
  15. 2022A survey of some recent developments in measures of associationcited by 3
  16. 2023Causal Discovery via Conditional Independence Testing with Proxy Variablescited by 3
  17. 2023Model-based causal feature selection for general response typescited by 3
  18. 2023Learning Generalizable Agents via Saliency-Guided Features Decorrelationcited by 3
  19. 2024The Power of Properties: Uncovering the Influential Factors in Emotion Classificationcited by 3
  20. 2024A Conditional Independence Test in the Presence of Discretizationcited by 3
  21. 2024General Frameworks for Conditional Two-Sample Testingcited by 3
  22. 2024Deep Nonparametric Conditional Independence Tests for Imagescited by 3
  23. 2021PPFS: Predictive Permutation Feature Selectioncited by 2
  24. 2022Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Datacited by 2
  25. 2025A Fast Kernel-based Conditional Independence test with Application to Causal Discoverycited by 2
  26. 2025Score-based Generative Modeling for Conditional Independence Testingcited by 2
  27. 2026The Generalised Kernel Covariance Measurecited by 2
  28. 2022Non-Parametric Inference of Relational Dependencecited by 1
  29. 2022Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Testscited by 1
  30. 2023Causal Razorscited by 1
  31. 2023Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Datacited by 1
  32. 2024When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Outcited by 1
  33. 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Datacited by 1
  34. 2024Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populationscited by 1
  35. 2024Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexitycited by 1
  36. 2025Quantum-enhanced causal discovery for a small number of samplescited by 1
  37. 2025A Sample Efficient Conditional Independence Test in the Presence of Discretizationcited by 1
  38. 2025Robust estimation of heterogeneous treatment effects in randomized trials leveraging external datacited by 1

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