arXiv Machine Learning June 17, 2026 By Eric V. Strobl

Fast Nonparametric Conditional Independence Testing via Two-Stage Regression

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Aug 12

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

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.

By Pavel Averin, Theodoros Moysiadis, Ioannis Katakis

efficiency

arXiv Machine Learning

Jul 14

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).

By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang

llms benchmarks

arXiv Machine Learning

Jun 3

Your Autoregressive Model Already Reveals the Causal Graph

arXiv:2602. 01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process.

By Hugo Math, Rainer Lienhart

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 AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

arXiv Machine Learning

22h ago

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

arXiv:2605. 31156v2 Announce Type: replace Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making.

By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye

llms benchmarks

Related stories

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

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

Your Autoregressive Model Already Reveals the Causal Graph

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

FoundCause: Causal Discovery with Latent Confounders from Observational Data

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery