Actual Causation and Nondeterministic Causal Models

Sander Beckers
Proceedings of the Fourth Conference on Causal Learning and Reasoning, PMLR 275:514-532, 2025.

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).

Cite this Paper

BibTeX
@InProceedings{pmlr-v275-beckers25a, title = {Actual Causation and Nondeterministic Causal Models}, author = {Beckers, Sander}, booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning}, pages = {514--532}, year = {2025}, editor = {Huang, Biwei and Drton, Mathias}, volume = {275}, series = {Proceedings of Machine Learning Research}, month = {07--09 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v275/main/assets/beckers25a/beckers25a.pdf}, url = {https://proceedings.mlr.press/v275/beckers25a.html}, 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).} }
Endnote
%0 Conference Paper %T Actual Causation and Nondeterministic Causal Models %A Sander Beckers %B Proceedings of the Fourth Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2025 %E Biwei Huang %E Mathias Drton %F pmlr-v275-beckers25a %I PMLR %P 514--532 %U https://proceedings.mlr.press/v275/beckers25a.html %V 275 %X 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).
APA
Beckers, S.. (2025). Actual Causation and Nondeterministic Causal Models. Proceedings of the Fourth Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 275:514-532 Available from https://proceedings.mlr.press/v275/beckers25a.html.

Related Material