mlsanityData to 7 Oct 2026

Conditional Independence Testing using Generative Adversarial Networks

arXiv 1907.04068 ↗First posted 9 Jul 2019stat.ML cs.LGcited by 36

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Abstract (arXiv)

We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces. Our contribution is a new test statistic based on samples from a generative adversarial network designed to approximate directly a conditional distribution that encodes the null hypothesis, in a manner that maximizes power (the rate of true negatives). We show that such an approach requires only that density approximation be viable in order to ensure that we control type I error (the rate of false positives); in particular, no assumptions need to be made on the form of the distributions or feature dependencies. Using synthetic simulations with high-dimensional data we demonstrate significant gains in power over competing methods. In addition, we illustrate the use of our test to discover causal markers of disease in genetic data.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2019.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 8; 2024: 5; 2025: 8; 2026: 3.

36 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2021Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspectivecited by 40
  3. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  4. 2022Model-X Sequential Testing for Conditional Independence via Testing by Bettingcited by 14
  5. 2021An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddingscited by 12
  6. 2022Composite Feature Selection using Deep Ensemblescited by 11
  7. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  8. 2023Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discoverycited by 10
  9. 2024Demystifying amortized causal discovery with transformerscited by 10
  10. 2021Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confoundingcited by 9
  11. 2022Conditional Independence Testing via Latent Representation Learningcited by 9
  12. 2022A unified framework for dataset shift diagnosticscited by 7
24 more
  1. 2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
  2. 2023Conditional independence testing under misspecified inductive biasescited by 7
  3. 2024Doubly Robust Conditional Independence Testing with Generative Neural Networkscited by 6
  4. 2023Nearest-Neighbor Sampling Based Conditional Independence Testingcited by 5
  5. 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacencycited by 5
  6. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  7. 2024Learning Identifiable Factorized Causal Representations of Cellular Responsescited by 4
  8. 2022DIET: Conditional independence testing with marginal dependence measures of residual informationcited by 3
  9. 2024Deep Nonparametric Conditional Independence Tests for Imagescited by 3
  10. 2021Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimationcited by 2
  11. 2022Learning to Increase the Power of Conditional Randomization Testscited by 2
  12. 2025Statistical Inference in Reinforcement Learning: A Selective Surveycited by 2
  13. 2025A Fast Kernel-based Conditional Independence test with Application to Causal Discoverycited by 2
  14. 2023Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Seriescited by 1
  15. 2025Amortized Conditional Independence Testingcited by 1
  16. 2025From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Mapscited by 1
  17. 2025Toward Scalable and Valid Conditional Independence Testing with Spectral Representationscited by 1
  18. 2021Secure PAC Bayesian Regression via Real Shamir Secret Sharingcited by 0
  19. 2023A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rulescited by 0
  20. 2025AICO: Feature Significance Tests for Supervised Learningcited by 0
  21. 2025A Conditional Distribution Equality Testing Framework using Deep Generative Learningcited by 0
  22. 2026Fast Flow Matching based Conditional Independence Tests for Causal Discoverycited by 0
  23. 2026PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Datacited by 0
  24. 2026seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequencescited by 0