arXiv AI June 24, 2026 By Sander Beckers

Difference-Making without Making a Difference

arXiv:2606. 24832v1 Announce Type: new Abstract: Over a series of seven papers, Andreas & G\"unther have introduced seven definitions of actual causation and have classified them as belonging to three different, competing, types of accounts: factual difference-making, counterfactual difference-making, and regularity-based.

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 AI.

arXiv AI

Jul 24

A Counterfactual Cause in Situation Calculus

arXiv:2501. 06857v3 Announce Type: replace Abstract: Perhaps the most popular modern formulation of actual causality is the HP account by Halpern and Pearl.

By Daxin Liu (Nanjing University), Vaishak Belle (The University of Edinburgh)

arXiv Machine Learning

Aug 14

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"

arXiv:2501. 05844v4 Announce Type: replace Abstract: Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality.

By Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sir, Martin Krutsky

arXiv AI

Jul 14

Referential Regimes: Transformation-Invariant Identity for Neutral Substrates

arXiv:2601. 16152v2 Announce Type: replace-cross Abstract: Data systems increasingly operate under persistent legal, political, and analytic disagreement, where no single interpretive authority can be assumed.

By Denise M. Case

arXiv AI

Jul 24

Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness

arXiv:2607. 20729v1 Announce Type: cross Abstract: A record system declares when two records refer to the same entity, occurrence, scope, or rule.

By Denise M. Case

safety

arXiv AI

Jun 2

Evaluating Bivariate Causal Statements Based on Mutual Compatibility

arXiv:2606. 00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess.

By Erik Jahn, Dominik Janzing

llms

arXiv AI

Jul 7

Failures and Successes to Learn a Core Conceptual Distinction from the Statistics of Language

arXiv:2607. 04523v1 Announce Type: cross Abstract: Generic statements like "tigers are striped" and "cars have radios" communicate information that is, in general, true.

By Zhimin Hu, Jeroen van Paridon, Gary Lupyan

llms

One email a morning, machine-written

Related stories

A Counterfactual Cause in Situation Calculus

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"

Referential Regimes: Transformation-Invariant Identity for Neutral Substrates

Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness

Evaluating Bivariate Causal Statements Based on Mutual Compatibility

Failures and Successes to Learn a Core Conceptual Distinction from the Statistics of Language