mlsanityData to 7 Oct 2026

Fast Flow Matching based Conditional Independence Tests for Causal Discovery

arXiv 2602.08315 ↗First posted 9 Feb 2026cs.LG stat.MLcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Constraint-based causal discovery methods require a large number of conditional independence (CI) tests, which severely limits their practical applicability due to high computational complexity. Therefore, it is crucial to design an algorithm that accelerates each individual test. To this end, we propose the Flow Matching-based Conditional Independence Test (FMCIT). The proposed test leverages the high computational efficiency of flow matching and requires the model to be trained only once throughout the entire causal discovery procedure, substantially accelerating causal discovery. According to numerical experiments, FMCIT effectively controls type-I error and maintains high testing power under the alternative hypothesis, even in the presence of high-dimensional conditioning sets. In addition, we further integrate FMCIT into a two-stage guided PC skeleton learning framework, termed GPC-FMCIT, which combines fast screening with guided, budgeted refinement using FMCIT. This design yields explicit bounds on the number of CI queries while maintaining high statistical power. Experiments on synthetic and real-world causal discovery tasks demonstrate favorable accuracy-efficiency trade-offs over existing CI testing methods and PC variants.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 24 of its 39 references that are in the map. First posted on arXiv 9 Feb 2026.

Builds on

24 of 39 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Score-based Generative Modeling for Conditional Independence Testing29 May
  2. 2024Conditional Diffusion Models Based Conditional Independence Testing16 Dec
  3. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 19

  1. 2022Flow Matching for Generative Modelingcited by 5,426
  2. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  3. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  4. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  5. 2016Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selectioncited by 120
  6. 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
  7. 2019Conditional Independence Testing using Generative Adversarial Networkscited by 36
  8. 2013The Randomized Dependence Coefficientcited by 24
  9. 2024FlowTS: Time Series Generation via Rectified Flowcited by 22
  10. 2020Double Generative Adversarial Networks for Conditional Independence Testingcited by 21
  11. 2021Significance tests of feature relevance for a black-box learnercited by 14
  12. 2024Causal Discovery with Fewer Conditional Independence Testscited by 13
7 more
  1. 2021An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddingscited by 12
  2. 2016Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-valuecited by 11
  3. 2025On the Hardness of Conditional Independence Testing In Practicecited by 6
  4. 2023Nearest-Neighbor Sampling Based Conditional Independence Testingcited by 5
  5. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  6. 2025Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Datacited by 2
  7. 2025From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Mapscited by 1

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