mlsanityData to 14 Oct 2026

An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings

arXiv 2110.14868 ↗First posted 28 Oct 2021stat.ML cs.LGcited by 12

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Abstract (arXiv)

We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of locations. We obtain its asymptotic distribution under the null hypothesis of conditional independence and design a consistent statistical test from it. We conduct a series of experiments showing that our new test outperforms state-of-the-art methods both in terms of type-I and type-II errors even in the high dimensional setting.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 28 Oct 2021.

Builds on

22 of 49 references.

References · 22

  1. 2016Generalization Properties of Learning with Random Featurescited by 164
  2. 2016Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selectioncited by 121
  3. 2017Sobolev Norm Learning Rates for Regularized Least-Squares Algorithmcited by 114
  4. 2020A Measure-Theoretic Approach to Kernel Conditional Mean Embeddingscited by 86
  5. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
  6. 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
  7. 2016Interpretable Distribution Features with Maximum Testing Powercited by 63
  8. 2015Fast Two-Sample Testing with Analytic Representations of Probability Measurescited by 53
  9. 2017Model-Powered Conditional Independence Testcited by 43
  10. 2017A Linear-Time Kernel Goodness-of-Fit Testcited by 41
  11. 2017Characteristic and Universal Tensor Product Kernelscited by 41
  12. 2019A simple measure of conditional dependencecited by 38
10 more
  1. 2019Conditional Independence Testing using Generative Adversarial Networkscited by 36
  2. 2016Large-Scale Kernel Methods for Independence Testingcited by 32
  3. 2020Double Generative Adversarial Networks for Conditional Independence Testingcited by 22
  4. 2020Kernel Partial Correlation Coefficient -- a Measure of Conditional Dependencecited by 21
  5. 2018Fast Conditional Independence Test for Vector Variables with Large Sample Sizescited by 16
  6. 2016An Adaptive Test of Independence with Analytic Kernel Embeddingscited by 13
  7. 2021Wasserstein Conditional Independence Testingcited by 7
  8. 2019Comparing distributions: ℓ1 geometry improves kernel two-sample testingcited by 5
  9. 2019On Distance and Kernel Measures of Conditional Independencecited by 5
  10. 2018Mimic and Classify : A meta-algorithm for Conditional Independence Testingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 6; 2026: 1.

12 papers.

  1. 2024Practical Kernel Tests of Conditional Independencecited by 15
  2. 2023Conditional independence testing under misspecified inductive biasescited by 7
  3. 2024Doubly Robust Conditional Independence Testing with Generative Neural Networkscited by 7
  4. 2025On the Hardness of Conditional Independence Testing In Practicecited by 7
  5. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  6. 2024Deep Nonparametric Conditional Independence Tests for Imagescited by 3
  7. 2025A Fast Kernel-based Conditional Independence test with Application to Causal Discoverycited by 2
  8. 2025Score-based Generative Modeling for Conditional Independence Testingcited by 2
  9. 2025Toward Scalable and Valid Conditional Independence Testing with Spectral Representationscited by 1
  10. 2022RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Featurescited by 0
  11. 2025Closing the Expression Gap in LLM Instructions via Socratic Questioningcited by 0
  12. 2026Fast Flow Matching based Conditional Independence Tests for Causal Discoverycited by 0