Efficient Discovery of Actual Causality with Uncertainty
Identifying the actual cause of events in engineered systems is a fundamental challenge in system analysis. Finding such causes becomes more challenging in the presence of noise and uncertainty in real-world systems. In this paper, we adopt the notion of probabilistic actual causality by Fenton-Glynn, which is a probabilistic extension of Halpern and Pearl's actual causality, and propose a novel method to formally reason about causal effect of events in systems subject to uncertainty. We (1) formulate the discovery of probabilistic actual causes in computing systems as an SMT problem, and (2) address the scalability challenges by introducing an abstraction-refinement technique that significantly improves efficiency. We demonstrate the effectiveness of our approach through three case studies, identifying probabilistic causes of safety violations in (1) the Mountain Car problem, (2) the Lunar Lander benchmark, and (3) MPC controller for an F-16 autopilot simulator.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Causal Kripke Models
This work extends Halpern and Pearl's causal models for actual causality to a possible world semantics environment. Using this framework we introduce a logic of actual causality with modal operators, which allows for rea…
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “G…
Causal InferenceTime SeriesTime Series AnalysisTowards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
Causal modelling offers great potential to provide autonomous agents the ability to understand the data-generation process that governs their interactions with the world. Such models capture formal knowledge as well as p…
Causal DiscoverycounterfactualActual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes
Actual causality and a closely related concept of responsibility attribution are central to accountable decision making. Actual causality focuses on specific outcomes and aims to identify decisions (actions) that were cr…
Decision MakingDecision Making Under UncertaintySequential Decision MakingAutomated Discovery of Functional Actual Causes in Complex Environments
Reinforcement learning (RL) algorithms often struggle to learn policies that generalize to novel situations due to issues such as causal confusion, overfitting to irrelevant factors, and failure to isolate control of sta…
AttributeReinforcement Learning (RL)