Actual Causation and Nondeterministic Causal Models
This work addresses the foundational issue of actual causation in causal inference, offering a more expressive framework, but it is incremental as it builds on prior definitions with similar outcomes.
The paper tackles the problem of defining actual causation by introducing a novel definition based on nondeterministic causal models, which generalizes Pearl's deterministic models, and shows that the resulting verdicts are almost identical to those from previous definitions.
In (Beckers, 2025) I introduced nondeterministic causal models as a generalization of Pearl's standard deterministic causal models. I here take advantage of the increased expressivity offered by these models to offer a novel definition of actual causation (that also applies to deterministic models). Instead of motivating the definition by way of (often subjective) intuitions about examples, I proceed by developing it based entirely on the unique function that it can fulfil in communicating and learning a causal model. First I generalize the more basic notion of counterfactual dependence, second I show how this notion has a vital role to play in the logic of causal discovery, third I introduce the notion of a structural simplification of a causal model, and lastly I bring both notions together in my definition of actual causation. Although novel, the resulting definition arrives at verdicts that are almost identical to those of my previous definition (Beckers, 2021, 2022).