mlsanityData to 7 Oct 2026

Deep Nonparametric Conditional Independence Tests for Images

arXiv 2411.06140 ↗First posted 9 Nov 2024stat.ML cs.LG eess.IVcited by 3

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Abstract (arXiv)

Conditional independence tests (CITs) test for conditional dependence between random variables. As existing CITs are limited in their applicability to complex, high-dimensional variables such as images, we introduce deep nonparametric CITs (DNCITs). The DNCITs combine embedding maps, which extract feature representations of high-dimensional variables, with nonparametric CITs applicable to these feature representations. For the embedding maps, we derive general properties on their parameter estimators to obtain valid DNCITs and show that these properties include embedding maps learned through (conditional) unsupervised or transfer learning. For the nonparametric CITs, appropriate tests are selected and adapted to be applicable to feature representations. Through simulations, we investigate the performance of the DNCITs for different embedding maps and nonparametric CITs under varying confounder dimensions and confounder relationships. We apply the DNCITs to brain MRI scans and behavioral traits, given confounders, of healthy individuals from the UK Biobank (UKB), confirming null results from a number of ambiguous personality neuroscience studies with a larger data set and with our more powerful tests. In addition, in a confounder control study, we apply the DNCITs to brain MRI scans and a confounder set to test for sufficient confounder control, leading to a potential reduction in the confounder dimension under improved confounder control compared to existing state-of-the-art confounder control studies for the UKB. Finally, we provide an R package implementing the DNCITs.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 34 of its 95 references that are in the map. First posted on arXiv 9 Nov 2024.

Builds on

34 of 95 references.

References · 34

  1. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  2. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 360
  3. 2020Explaining by Removing: A Unified Framework for Model Explanationcited by 218
  4. 2020Learning Deep Kernels for Non-Parametric Two-Sample Testscited by 129
  5. 2019FastSurfer -- A fast and accurate deep learning based neuroimaging pipelinecited by 110
  6. 2016Generative Models and Model Criticism via Optimized Maximum Mean Discrepancycited by 99
  7. 2014Controlling the false discovery rate via knockoffscited by 97
  8. 2015On the Error of Random Fourier Featurescited by 76
  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. 2017Using simulation studies to evaluate statistical methodscited by 47
  12. 2020A general framework for inference on algorithm-agnostic variable importancecited by 44
22 more
  1. 2017Model-Powered Conditional Independence Testcited by 43
  2. 2019A simple measure of conditional dependencecited by 38
  3. 2019Conditional Independence Testing using Generative Adversarial Networkscited by 36
  4. 2019Testing Conditional Independence in Supervised Learning Algorithmscited by 27
  5. 2019Two-sample Testing Using Deep Learningcited by 26
  6. 2021Local permutation tests for conditional independencecited by 23
  7. 2020Double Generative Adversarial Networks for Conditional Independence Testingcited by 21
  8. 2020Kernel Partial Correlation Coefficient -- a Measure of Conditional Dependencecited by 21
  9. 2021Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Reviewcited by 19
  10. 2022Efficient Conditionally Invariant Representation Learningcited by 19
  11. 2018Fast Conditional Independence Test for Vector Variables with Large Sample Sizescited by 16
  12. 2022The Projected Covariance Measure for assumption-lean variable significance testingcited by 16
  13. 2021Significance tests of feature relevance for a black-box learnercited by 14
  14. 2018A Tour of Unsupervised Deep Learning for Medical Image Analysiscited by 13
  15. 2021The Weighted Generalised Covariance Measurecited by 13
  16. 2021Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysiscited by 12
  17. 2021An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddingscited by 12
  18. 2021On boosting the power of Chatterjee's rank correlationcited by 10
  19. 2022Conditional Independence Testing via Latent Representation Learningcited by 9
  20. 2021Statistical quantification of confounding bias in predictive modellingcited by 6
  21. 2020On the power of conditional independence testing under model-Xcited by 4
  22. 2017Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modellingcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speechescited by 1
  2. 2026Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Predictioncited by 0
  3. 2026ICON Decomposition: Auditing deep neural networks for shortcuts by decomposing layer-wise representations using conceptscited by 0