Grant: Discovery Grants Program - Individual

Research on actual causality involves finding in a given log the actions or events that caused an observed effect. Causality analysis plays a crucial role in automated reasoning and has numerous applications in practically every field, including computer science (e.g. for databases, program verification, explainable artificial intelligence, etc.), manufacturing and engineering, medicine and health science (e.g. for diagnostic purposes), and tort law (e.g. to assign responsibility and blame), to name a few. For instance, if an aircraft crashes, it is useful to analyze the actions captured by the flight-recorder and identify those that led to this disaster. Philosophers since the time of Aristotle have been grappling with this basic question of what actually caused an effect, but a proper definition that is general enough is yet to be proposed. It turns out that actual causality in general is extremely tricky to formulate. Current formal approaches to actual causation are based on Structural Equations Models (SEMs). Although very popular, these models have limited expressiveness and suffer from a variety of problems. This research program aims at overcoming some of the challenges involved in the formalization of actual causation. To this end, my students and I will develop a comprehensive theory of actual causation that is based on a formal theory of action and change, and investigate its potential applications. In the short term, I will pursue three main objectives. First, I will develop a definition of actual cause within discrete dynamical systems. I will ensure that my formalization can support non-linear scenarios, i.e. those where the observed events are only partially ordered, and model causation from both an objective perspective and from the perspective of individual agents. Secondly, I will study causality in hybrid dynamic domains where change can involve discrete event occurrences as well as can be a result of the flow of time and be dictated by some continuous function. Finally, I will investigate various applications of this theory. In particular, I will apply causation for the diagnosis of faults in energy systems. Moreover, I will tackle two other particularly impactful indirect applications, namely the explanation of agent behaviour using root cause analysis, and the attribution of responsibility and blame in multi-agent systems. Action theoretic formalizations of actual cause are seen by many as a key technology for overcoming the limitations of current proposals. The outcome of the research program will contribute to the development of formal theories as well as software tools for analyzing actual causation in a variety of practical domains. My students will develop causality theories, implement causal engines based on these, and evaluate them through carefully designed theorems and experiments, accumulating skills along the way that will prepare them well for industry and academia.