Causal Stacks: A Theoretical Framework for Recurrent and Hierarchical Counterfactual Reasoning
Bibliographic record
Abstract
Counterfactual (CF) reasoning – the process of considering alternative events and their outcomes – plays a vital role in understanding causation in fields like cognitive psychology and philosophy of science. In this paper, I develop a theoretical framework of Structural Causal Stacks (SCS) that provides a conceptual structure to describe the relationships between related causal and counterfactual analyses. Then, I explore its useability for observing human reasoning by running 500 pilot simulations of causal stack agents. My simulation modelled Gerstenberg et al. (2013)’s experiment design, which measured whether people’s judgements about the consequence of a counterfactual state changes depended on the order they considered the events. According to my preliminary results, the stack model replicated the asymmetry in backwards versus forward counterfactual reasoning, aligning with the established consensus in a cognitive psychology literature while extending a persistent explanation for successive analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".