Actual Causation and Nondeterministic Causal Models
Sander Beckers
TL;DR
This paper develops a functional account of actual causation within nondeterministic Structural Causal Models (NSCMs), generalizing Pearl's deterministic framework. It ties the notion to the role of counterfactual and interventional dependence and integrates a causal-discovery logic with the idea of structural simplifications to determine when one event is the actual cause of another. The approach yields verdicts compatible with prior deterministic accounts while extending to nondeterministic and potentially probabilistic settings, offering a robust method for communicating, learning, and discovering causal structure. The framework promises clearer criteria for causation, with potential applications to Causal Bayesian Networks and broader causal reasoning tasks.
Abstract
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).