arXiv AI July 21, 2026 By Olav Benjamin Vassend

A Causal Markov Condition for Value

arXiv:2607. 16717v1 Announce Type: cross Abstract: This paper proposes a causal independence principle for value -- the value Causal Markov Condition (v-CMC) -- and develops the conceptual and mathematical foundations of a "causal value theory" linking causality and utility.

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

Aug 14

Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds

arXiv:2505. 15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making.

By Xin Shu, Shuai Wang, Ang Li

arXiv AI

Jul 31

Property-driven Causal Abstractions for Markov Decision Processes

arXiv:2607. 26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations.

By Jule Schmidt, Maximilian Weininger, Clemens Dubslaff, David Parker, Nils Jansen

reinforcement-learning benchmarks

arXiv Statistics ML

2d ago

Notes on Forr\'e's Notion of Conditional Independence and Causal Calculus for Continuous Variables

arXiv:2603.24333v2 Announce Type: replace-cross Abstract: Recently, Forr\'e (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provid...

By Leihao Chen

arXiv AI

Aug 21

Causal Reasoning with Bipartite Graphical Causal Models

arXiv:2608. 19831v1 Announce Type: new Abstract: Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they cannot adequately represent all real-world causal systems.

By Joris M. Mooij

arXiv AI

Jul 21

Generalised Bellman recurrence and three dualities in sequential decision-making

arXiv:2607. 18077v1 Announce Type: cross Abstract: What gives the Bellman equation its form?

By Fernando E. Rosas, David Hyland, Daniel Polani

reinforcement-learning

arXiv AI

Jun 11

Planning under Distribution Shifts with Causal POMDPs

arXiv:2602. 23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts.

By Matteo Ceriscioli, Karthika Mohan

reinforcement-learning

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Related stories

Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds

Property-driven Causal Abstractions for Markov Decision Processes

Notes on Forr\'e's Notion of Conditional Independence and Causal Calculus for Continuous Variables

Causal Reasoning with Bipartite Graphical Causal Models

Generalised Bellman recurrence and three dualities in sequential decision-making

Planning under Distribution Shifts with Causal POMDPs