Explanatory Power as a Substitute for Statistical Reasoning
Bibliographic record
Abstract
People judge the strength of cause-and-effect relationships as a matter of routine, and often do so in the absence of evidence about the covariation between cause and effect. Here, we examine the possibility that explanatory power is used as a heuristic for making these judgments. Our argument proceeds in three steps. First, we show that explanatory power and causal strength judgments for sets of historical events are almost perfectly correlated (Study 1). Next, we intervene on explanatory power without changing the target causal relation by manipulating explanatory scope—the number of effects predicted by an explanation. Scope manipulations lead to downstream consequences for causal strength judgments (Study 2), supported by item-by-item correlations between causal strength and explanatory power (Study 3). Finally, we show that explanatory power has causal signatures even in the non-causal domain of mathematics (Study 4). These results suggest that explanatory power may be a useful heuristic for estimating causal strength in the absence of statistical evidence.
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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 | 0.034 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".