A Comprehensive Collection of Vignettes for Actual Causation

Christian Odenwald
Proceedings of the Fifth Conference on Causal Learning and Reasoning, PMLR 323:911-931, 2026.

Abstract

Theories of actual causation provide answers to the question: “Is C a cause of E?” in a specific scenario. The performance of a new theory is measured by how well its verdicts agree with the intuitive verdicts of the researcher on particular examples, commonly referred to as vignettes. This has two drawbacks: First, this is usually done only for a handful of vignettes per theory since there is no commonly agreed-upon collection of vignettes. That makes it difficult to compare theories against each other. Second, this evaluation is mostly done by hand. That makes it tedious for both the researcher proposing a new theory and the reader who tries to assess the merits of the new theory. To solve this, we provide a comprehensive collection of vignettes in a well-organized data format. We provide code to load these vignettes and accompanying queries. We also provide an implementation of two popular theories of causation to demonstrate the advantage of this approach. In addition, we address the suggestion that LLMs might be more suitable than formal models of these vignettes to determine causality. To test this claim on current LLMs, we add formulations of vignettes and queries in natural language. That makes it possible to prompt LLMs for their verdict and compare their results both with intuitions and the verdicts of particular theories of actual causation. We find that none of the tested LLMs achieves higher performance than either of the two implemented theories of causation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v323-odenwald26a, title = {A Comprehensive Collection of Vignettes for Actual Causation}, author = {Odenwald, Christian}, booktitle = {Proceedings of the Fifth Conference on Causal Learning and Reasoning}, pages = {911--931}, year = {2026}, editor = {Mazaheri, Bijan and Hanson, Niels Richard}, volume = {323}, series = {Proceedings of Machine Learning Research}, month = {06--08 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v323/main/assets/odenwald26a/odenwald26a.pdf}, url = {https://proceedings.mlr.press/v323/odenwald26a.html}, abstract = {Theories of actual causation provide answers to the question: “Is C a cause of E?” in a specific scenario. The performance of a new theory is measured by how well its verdicts agree with the intuitive verdicts of the researcher on particular examples, commonly referred to as vignettes. This has two drawbacks: First, this is usually done only for a handful of vignettes per theory since there is no commonly agreed-upon collection of vignettes. That makes it difficult to compare theories against each other. Second, this evaluation is mostly done by hand. That makes it tedious for both the researcher proposing a new theory and the reader who tries to assess the merits of the new theory. To solve this, we provide a comprehensive collection of vignettes in a well-organized data format. We provide code to load these vignettes and accompanying queries. We also provide an implementation of two popular theories of causation to demonstrate the advantage of this approach. In addition, we address the suggestion that LLMs might be more suitable than formal models of these vignettes to determine causality. To test this claim on current LLMs, we add formulations of vignettes and queries in natural language. That makes it possible to prompt LLMs for their verdict and compare their results both with intuitions and the verdicts of particular theories of actual causation. We find that none of the tested LLMs achieves higher performance than either of the two implemented theories of causation.} }
Endnote
%0 Conference Paper %T A Comprehensive Collection of Vignettes for Actual Causation %A Christian Odenwald %B Proceedings of the Fifth Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2026 %E Bijan Mazaheri %E Niels Richard Hanson %F pmlr-v323-odenwald26a %I PMLR %P 911--931 %U https://proceedings.mlr.press/v323/odenwald26a.html %V 323 %X Theories of actual causation provide answers to the question: “Is C a cause of E?” in a specific scenario. The performance of a new theory is measured by how well its verdicts agree with the intuitive verdicts of the researcher on particular examples, commonly referred to as vignettes. This has two drawbacks: First, this is usually done only for a handful of vignettes per theory since there is no commonly agreed-upon collection of vignettes. That makes it difficult to compare theories against each other. Second, this evaluation is mostly done by hand. That makes it tedious for both the researcher proposing a new theory and the reader who tries to assess the merits of the new theory. To solve this, we provide a comprehensive collection of vignettes in a well-organized data format. We provide code to load these vignettes and accompanying queries. We also provide an implementation of two popular theories of causation to demonstrate the advantage of this approach. In addition, we address the suggestion that LLMs might be more suitable than formal models of these vignettes to determine causality. To test this claim on current LLMs, we add formulations of vignettes and queries in natural language. That makes it possible to prompt LLMs for their verdict and compare their results both with intuitions and the verdicts of particular theories of actual causation. We find that none of the tested LLMs achieves higher performance than either of the two implemented theories of causation.
APA
Odenwald, C.. (2026). A Comprehensive Collection of Vignettes for Actual Causation. Proceedings of the Fifth Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 323:911-931 Available from https://proceedings.mlr.press/v323/odenwald26a.html.

Related Material