arXiv AI
Jun 24
Infinitesimal Causality
arXiv:2606. 24621v1 Announce Type: cross Abstract: This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics.
Infinitesimal Causality
This paper introduces a categorical account of infinitesimal causality in Frobenius Markov categories equipped with tangent-bundle semantics. IDC captures the infinitesimal layer in which interventions act as tangent deformations of copy/discard structure.
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arXiv AI
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Latent Confounded Causal Discovery via Lie Bracket Geometry
arXiv:2606. 19610v1 Announce Type: cross Abstract: Recent work on Kan-Do-Calculus (KDC) has established that the boundary between passive observation and active intervention in causal inference is a category-theoretic bi-adjunction, with interventions modeled by left Kan extensions and conditioning by right Kan extensions.
arXiv AI
Jul 24
On the Granularity of Causal Effect Identifiability
arXiv:2510. 16703v3 Announce Type: replace-cross Abstract: The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables.
arXiv AI
Aug 12
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
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arXiv AI
Jun 2
Topological Ignorability for Structural Causal Effects Beyond Means
arXiv:2606. 01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create loops or holes, generate branches, or reorganize an outcome cloud while leaving the average response nearly unchanged.
arXiv Machine Learning
Jul 28
Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations
arXiv:2607. 22910v1 Announce Type: new Abstract: Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal reasoning.
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.
arXiv AI
Jun 9
Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
arXiv:2606. 08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure.
arXiv AI
Jun 2
Causal Density Functions
arXiv:2606. 00754v1 Announce Type: cross Abstract: We introduce causal density functions: Radon-Nikodym derivatives that compare interventional laws to observational laws and therefore act as local density ratios for causal effects.
Hugging Face Trending Papers
Jun 23
An Introduction to Causal Reinforcement Learning
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i. e.
arXiv Machine Learning
Jul 10
Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems
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arXiv AI
Jul 16
Partially Observed Structural Causal Models
arXiv:2605. 03268v2 Announce Type: replace-cross Abstract: Here we introduce Partially Observed Structural Causal Models (POSCMs) as an extension of structural causal models (SCMs) to settings where upstream contexts co-determine both the interaction structure and downstream mechanisms on observed variables.