A Causal Model Checker for Legal Cases

Causation plays a central role in the attribution of responsibility, especially in the legal domain, where complex causal scenarios frequently arise. Traditionally, legal reasoners have relied on the idea that a cause must be a necessary condition of its effect, which falls short in scenarios involving overdetermination, preemption, or omission, thereby failing to adequately identify causes-in-fact. In this paper, we present a novel analysis of selected legal cases, each exemplifying common causal dilemmas discussed in causal literature. We employ three different notions of cause in our analysis: abductive explanation (AXp), the NESS test (Necessary Element of a Sufficient Set) and actual cause. We express the three notions and some of their variants in a modal language for causal reasoning that we interpret on a rule-based semantics. We provide a model checking algorithm for our modal language relying on a reduction into TQBF as well as an implementation of the legal cases in our causal model checker to automatically verify "what is the cause of what"and what types of causes apply in each legal case. Our interdisciplinary approach highlights the usefulness of logic-based methods for legal analysis, offering a fully transparent model-checking toolbox that could potentially support legal reasoners in disentangling complex factual scenarios.