# From Verification to Causality-based Explications

arXiv: https://arxiv.org/abs/2105.09533 · HTML: https://mlsanity.com/paper/2105.09533/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2021.

## Built on by (most cited first, 12 of 12)

- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective](https://mlsanity.com/paper/2303.00244/) (2023)
- [Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents](https://mlsanity.com/paper/2402.16905/) (2024)
- [Causal Temporal Reasoning for Markov Decision Processes](https://mlsanity.com/paper/2212.08712/) (2022)
- [Interactive Explanations for Reinforcement-Learning Agents](https://mlsanity.com/paper/2504.05393/) (2025)
- [Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access](https://mlsanity.com/paper/2303.12718/) (2023)
- [CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systems](https://mlsanity.com/paper/2602.02138/) (2026)
- [ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents](https://mlsanity.com/paper/2301.09941/) (2023)
- [Causal Kripke Models](https://mlsanity.com/paper/2307.05631/) (2023)
- [A Counterfactual Cause in Situation Calculus](https://mlsanity.com/paper/2501.06857/) (2025)
- [Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees](https://mlsanity.com/paper/2606.29681/) (2026)
- [Property-driven Causal Abstractions for Markov Decision Processes](https://mlsanity.com/paper/2607.26787/) (2026)

## 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.