arXiv Machine Learning August 12, 2026 By Pavel Averin, Theodoros Moysiadis, Ioannis Katakis

Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey

Read the original on arXiv Machine Learning →

arXiv:2608. 11156v1 Announce Type: cross Abstract: Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

Jun 17

Fast Nonparametric Conditional Independence Testing via Two-Stage Regression

arXiv:2606. 18011v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships.

By Eric V. Strobl

safety

arXiv Machine Learning

Jun 5

Toward Scalable and Valid Conditional Independence Testing with Spectral Representations

arXiv:2512. 19510v2 Announce Type: replace Abstract: Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions.

By Alek Fr\"ohlich, Vladimir R. Kostic, Karim Lounici, Daniel Perazzo, Daniel Tiezzi, Massimiliano Pontil

arXiv Machine Learning

Jul 14

Relaxing Faithfulness with Intervention-Only Causal Discovery

arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.

By Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv Machine Learning

Jun 24

GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series

arXiv:2606. 23880v1 Announce Type: new Abstract: From climate teleconnections to gene regulation, modern time-series datasets encompass tens or hundreds of interacting variables, making causal discovery increasingly challenging.

By Mohammad Fesanghary, Abhinav Havaldar

benchmarks

arXiv Machine Learning

Sep 22

Decoupled Causal Discovery

arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...

By Zhengkang Guan, Fei Wu, Kun Kuang

One email a morning, machine-written

Related stories

Fast Nonparametric Conditional Independence Testing via Two-Stage Regression

Toward Scalable and Valid Conditional Independence Testing with Spectral Representations

Relaxing Faithfulness with Intervention-Only Causal Discovery

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series

Decoupled Causal Discovery