Conditional Independence Testing

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Domain-Adapted Diffusion Models for Conditional Independence Testing
Sep 27, 2026Yanfeng Yang, Junda Zhao, Yijie Gao +8Multi-Source Domain Adaptation Domain Adaptation
The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan · The Institute of Statistical Mathematics, Tokyo, Japan · East China Normal University, Shanghai, China +4
Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment
Sep 1, 2026Mian Zhong, Katherine A. Keith, Anjalie FieldCausal Effect Estimation Sparse Autoencoders
Johns Hopkins University · Williams College / Cohere · Cohere
Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches
Sep 1, 2026Marco Simnacher, Georg Keilbar, Benjamin König +2LLM Evaluation Privacy Leakage in Language Models
Chair of Statistics Humboldt-Universität zu Berlin Spandauer Str. 1, 10178 Berlin, Germany · Chair Digital Health & Machine Learning Hasso-Plattner-Institut for Digital Engineering Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam, Germany
Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence
Aug 13, 2026Varun Pratap Bhardwaj, Garima Singh, Arun Pratap BhardwajAgent Failure Analysis AI Agent Reliability
Qualixar / Independent Researcher, India · Independent Researcher, India
Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey
Aug 11, 2026Pavel Averin, Theodoros Moysiadis, Ioannis KatakisCausal Discovery Causal Inference
Department of Computer Science School of Sciences and Engineering University of Nicosia 2417, Nicosia, Cyprus
Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series
Jul 27, 2026Mohammad FesangharyTime-Series Causal Discovery Causal Discovery
Bloomberg LP, New York, NY, USA
Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
Jun 30, 2026Eric Shen, Tony Wu, Mahbod Majid +1Graph Structure Learning Probabilistic Graphical Models
MIT
Sequential Kernel-based Conditional Independence Testing via Adaptive Betting
Jun 17, 2026Zheng He, Danica J. SutherlandKernel Methods Conditional Independence Testing
Department of Computer Science, University of British Columbia, Vancouver, Canada · Alberta Machine Intelligence Institute, Edmonton, Canada
Fast Nonparametric Conditional Independence Testing via Two-Stage Regression
Jun 16, 2026Eric V. StroblCausal Discovery Causal Structure Learning
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15213, USA
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
May 7, 2026Joseph D. RamseyFourier Feature Embeddings Causal Discovery
Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
May 6, 2026Thomas S. Robinson, Ranjit LallIncomplete Data Imputation Causal Discovery
Department of Methodology London School of Economics and Political Science London, UK · Department of Politics and International Relations University of OxfordMay Oxford, UK
Meta-Dependence in Conditional Independence Testing
Apr 17, 2025Bijan Mazaheri, Jiaqi Zhang, Caroline UhlerConditional Independence Testing
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

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Domain-Adapted Diffusion Models for Conditional Independence Testing

Yanfeng Yang, Junda Zhao, Yijie Gao, Jiaqi Yang+7

The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan · The Institute of Statistical Mathematics, Tokyo, Japan · East China Normal University, Shanghai, China+4

Multi-Source Domain Adaptation Domain Adaptation Conditional Distribution Estimation Conditional Independence Testing Conditional Diffusion Models

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Conditional independence (CI) is a fundamental concept in statistics and machine learning. Recent advances in conditional generative modeling provide flexible tools for generative-model-based CI tests, which rely on an estimated conditional distribution to generate randomized samples. However, errors in estimating this distribution accumulate in existing Type I error bounds, and consistency of the generative estimator alone does not guarantee asymptotic Type I error control. To address this limitation, we formulate conditional generative modeling as a domain adaptation problem and leverage auxiliary data from multiple source domains to improve estimation in the target CI testing domain. We propose Domain-Adapted Diffusion (DA-Diff), a multi-source domain adaptation framework for conditional diffusion models based on weighted empirical risk minimization over both target and source domains. We establish the convergence rate of DA-Diff and show how transferable source data can improve target-domain estimation through an increased effective sample size while controlling transfer bias. Building on DA-Diff, we further propose Domain-Adapted Conditional Independence Testing (DA-CIT) and show that its Type I error satisfies

P(p \leq α) \leq α+ o(1)

. Experiments demonstrate that DA-Diff improved conditional generation quality compared with transfer-learning diffusion baselines, while DA-CIT provides strong Type I error control and competitive power.

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