Situation Calculus Semantics for Actual Causality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The definitions of actual cause given by Pearl and Halpern (HP) in the framework of causal models provided vital computational insight into an old philosophical problem but by no means resolved it. One source of concern is the lack of objective criteria for selecting possible worlds to be admitted into the counterfactual analysis, epitomized by the competition between multiple proposals by HP and others. Another concern is due to the modest expressivity of propositional-level structural equations which limits their applicability and, arguably, contributes to the the former problem. We tackle both of these issues using a novel approach. We build our definition of actual cause from first principles in the context of atemporal situation calculus (SC) action theories with sequential actions. As a result, we can successfully identify actual causes of conditions expressed in first-order logic. We validate the HP approach by providing a formal translation from causal models to SC and proving a relationship between our definitions of actual cause and that of HP. Using well-known and new examples, we show that long-standing disagreements between alternative definitions of actual causality can be mitigated by faithful SC modelling of the domains.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it