From Verification to Causality-based Explications
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Abstract (arXiv)
In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl's notion of actual causation inspired verification-oriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2021.
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Citing papers per year: 2022: 2; 2023: 4; 2024: 1; 2025: 2; 2026: 3.
12 papers.
- 2022Applications of statistical causal inference in software engineeringcited by 12
- 2023SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspectivecited by 5
- 2024Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agentscited by 5
- 2022Causal Temporal Reasoning for Markov Decision Processescited by 3
- 2025Interactive Explanations for Reinforcement-Learning Agentscited by 2
- 2023Strategy Synthesis in Markov Decision Processes Under Limited Sampling Accesscited by 1
- 2026CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systemscited by 1
- 2023ASQ-IT: Interactive Explanations for Reinforcement-Learning Agentscited by 0
- 2023Causal Kripke Modelscited by 0
- 2025A Counterfactual Cause in Situation Calculuscited by 0
- 2026Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guaranteescited by 0
- 2026Property-driven Causal Abstractions for Markov Decision Processescited by 0
- 2026Property-driven Causal Abstractions for Markov Decision Processes29 Jul 2026
- 2026Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees29 Jun 2026
- 2026CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systems2 Feb 2026
- 2025Interactive Explanations for Reinforcement-Learning Agents7 Apr 2025
- 2025A Counterfactual Cause in Situation Calculus12 Jan 2025
- 2024Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents24 Feb 2024
- 2023Causal Kripke Models11 Jul 2023
- 2023Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access22 Mar 2023
- 2023SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective1 Mar 2023
- 2023ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents24 Jan 2023
- 2022Causal Temporal Reasoning for Markov Decision Processes16 Dec 2022
- 2022Applications of statistical causal inference in software engineering21 Nov 2022