mlsanityData to 14 Oct 2026

Testing Conditional Distribution Equality Using Generative Models

arXiv 2610.20447 ↗First posted 14 Oct 2026stat.ME stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a new test that leverages generative models to compare conditional distributions using both observed data and synthetic samples. A sample-splitting and studentization scheme is introduced to control estimation error. Under the null, the test statistic is asymptotically normal, and we establish local power against contiguous alternatives. Our theory under the null and fixed alternative requires only consistency of the learned conditional distribution, allowing for a broad class of generative models. Empirical results demonstrate accurate size control and strong power in high-dimensional settings compared to representative kernel-based conditional and joint two-sample tests.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 14 Oct 2026.

Builds on

24 of 38 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  4. 2019The frontier of simulation-based inferencecited by 502
  5. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 429
  6. 2015Generative Moment Matching Networkscited by 257
  7. 2015Training generative neural networks via Maximum Mean Discrepancy optimizationcited by 151
  8. 2023Engression: Extrapolation through the Lens of Distributional Regressioncited by 51
  9. 2018Deep Knockoffscited by 41
  10. 2021Diagnostics for Conditional Density Models and Bayesian Inference Algorithmscited by 28
  11. 2020Double Generative Adversarial Networks for Conditional Independence Testingcited by 22
  12. 2023Statistical Inference for Fairness Auditingcited by 20
12 more
  1. 2020A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sumcited by 19
  2. 2020Deep Dimension Reduction for Supervised Representation Learningcited by 15
  3. 2022Evaluating Aleatoric Uncertainty via Conditional Generative Modelscited by 10
  4. 2022Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testingcited by 7
  5. 2024A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)cited by 7
  6. 2024Doubly Robust Conditional Independence Testing with Generative Neural Networkscited by 7
  7. 2019Distance-based and RKHS-based Dependence Metrics in High Dimensioncited by 5
  8. 2025Enhancing Causal Effect Estimation with Diffusion-Generated Datacited by 4
  9. 2025Conditional Feature Importance with Generative Modeling Using Adversarial Random Forestscited by 3
  10. 2025From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Mapscited by 2
  11. 2026Testing Equality of Conditional Distributions via Generative Modelscited by 2
  12. 2026Measuring Differences between Conditional Distributions using Kernel Embeddingscited by 1

Built on by

Not cited yet.