arXiv AI
Jul 24
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
arXiv:2607. 21208v1 Announce Type: new Abstract: Pearl famously argues that causal knowledge enables the prediction of intervention effects.
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conclusions drawn from observations.
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arXiv AI
Aug 10
From probability to causality in probabilistic logic programming
arXiv:2608. 07230v1 Announce Type: new Abstract: Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system.
arXiv AI
Jul 24
Logic Programming Semantics for Causal Processes
arXiv:2607. 21233v1 Announce Type: new Abstract: Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual states of causal processes compatible with those logic programs.
arXiv AI
Sep 4
Symmetries and Causality: Causal Effect Identification Beyond IID Data
The paper introduces a formal framework that uses symmetries in data to keep causal mechanisms invariant, providing a simple and general mathematical language for causal reasoning. It outlines how to describe models and queries, and presents strategies for rigorously identifying causal effects from data within this framework. The approach reproduces known results for IID data and extends causal analysis to non‑IID settings, complex queries beyond do‑ or soft‑interventions, and incorporates missing data, transfer, and robustness considerations.
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.
arXiv AI
Jun 29
Lifted Causal Inference
arXiv:2606. 28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
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.
arXiv AI
Aug 12
Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.
arXiv AI
Jun 30
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
arXiv:2606. 29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur.
Hugging Face Trending Papers
Jun 29
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur. Probability-raising (PR) causality addresses this by identifying states whose visitation increases the probability of reaching designated states.
arXiv AI
Sep 4
A Computationally Feasible Framework for Causal Probabilistic Explanation
The paper introduces Probabilistic Causal Impact (PCI), a framework that blends actual causality (AC) with Pearl’s probability of necessity and sufficiency to provide tractable, causally grounded explanations. PCI reframes explainability as an estimation problem on a probabilistic causal model, enabling efficient approximation via Monte Carlo sampling. The authors evaluate PCI on synthetic and real-world data, demonstrating consistency with AC, scalability, and applicability to complex continuous systems and large-scale causal machine learning models.
Hugging Face Trending Papers
Jul 14
Solution of the Hempel's statistical ambiguity problem and Causal AI
This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws. To avoid such predictions, Carl Hempel proposed the Requirement of Maximal Specificity (RMS) for the statistical laws used in the inference.