Ambiguous Cause-in-Fact and Structured Causation: A Multi-Jurisdictional Approach
Bibliographic record
Abstract
Understanding the application of judicially created cause-in-fact doctrine to a case where proof of cause is at best ambiguous, necessitates understanding what has driven the court to tinker with existing tort doctrine in the first place. It is the aim of this article to explore .how these three modifications to traditional cause-in-fact principles operate, examine why they arose, and then use a normative lens to evaluate why they need to be consolidated into a predictable and portable outgrowth of causation doctrine.This article is divided into four substantive sections. Part II defines the landscape of cause-in-fact doctrine and explains how American, Canadian, and British courts have modified this landscape to oblige ambiguous causation cases. Part III evaluates the judicial modifications of cause-in-fact doctrine and explores the often inarticulated motivations that drive a court's use of these innovations. First, reversal of the burden of proof of causation from the plaintiff to the defendant is considered. Next, the approach that proves cause based on the defendant's material increase of risk of injury to the plaintiff is evaluated. And finally, the practice of proving causation based on reasonable inferences on the facts of the case is examined. Part IV is the pivotal division of the article, where the three alternative approaches to cause-in-fact are synthesized to produce a new, normative3 method for deciding ambiguous cause-in-fact cases. This method, called structured causation, deifies existing torts principles in a fashion that is also compatible with a positivist interpretation of what courts have been doing. Part V explains how structured causation accomplishes the normative goals, which are compatible with the competing tort theories of efficiency and corrective justice.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".