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.
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.
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arXiv Machine Learning
5d ago
"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.
arXiv AI
Aug 10
Explicit, Not Longer: What Makes Epistemic Stance Survive Memory Compression
arXiv:2608. 06953v1 Announce Type: cross Abstract: Agent memory systems compress what they store, and compression is built to drop qualifiers, so a claim's epistemic standing tends not to survive being written to memory.
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.
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.
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.
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.
arXiv AI
Jul 9
Neutral Substrates: A Design Constraint for Shared Records Under Persistent Interpretive Disagreement
arXiv:2601. 14271v2 Announce Type: replace Abstract: Shared accountability records are often used by parties who may never agree about causation, responsibility, or normative interpretation.
arXiv AI
Jun 17
CausalT5k: Diagnosing Refusal and Failure Modes in Trustworthy Causal Reasoning Across Causal Rungs
arXiv:2602. 08939v2 Announce Type: replace Abstract: Large language models increasingly produce fluent causal explanations, yet they often fail in ways aggregate accuracy cannot diagnose: confusing association with intervention, abandoning correct judgments under pressure, over-refusing valid claims, or answering when evidence is underdetermined.
arXiv Machine Learning
Jun 5
Trust, but Don't Verify: Epistemic Blind Spots in LLM Source Evaluation
arXiv:2606. 05403v1 Announce Type: new Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions.
arXiv AI
Jul 2
Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations
arXiv:2503. 13445v3 Announce Type: replace-cross Abstract: When asked to explain their decisions, LLMs can often give explanations which sound plausible to humans.
arXiv AI
Jun 16
Vernier: Probing Representational Misalignment Behind Lexical Gaps in Causal Reasoning
arXiv:2606. 15733v1 Announce Type: cross Abstract: Instruction-tuned language models can answer the same causal-reasoning question differently after its English variable names are replaced by type-preserving placeholders, although the structural causal model and the gold answer are unchanged.