Causal Interpretations of Correlational Evidence Regarding Violence
Bibliographic record
Abstract
Inferring causation from correlation can lead to erroneous explanations of violent behavior and the development and implementation of ineffective or even harmful interventions and policies. This article explores the inferences that violence researchers draw from evidence related to violent offending. We invited authors of articles published in violence journals to complete an online survey in which they were asked to identify a factor that may be a cause of violence, cite a study that demonstrates the factor is associated with violence, and provide their inferences from that study. We read each study and coded its research design (description of a sample [ n = 9], cross-sectional/retrospective non-experiment [ n = 18], single-wave longitudinal non-experiment [ n = 10], multi-wave longitudinal non-experiment [ n = 0], or randomized experiment [ n = 5]) and the appropriate inferences (inter-rater reliability was adequate; κ = 0.73–1.00). Reassuringly, participants ( N = 42; 57.1% in United States; 59.5% women) rarely indicated that their identified study demonstrated that their factor was a cause of violence (0.0%–16.7%) when the study was not a randomized experiment. However, many participants failed to acknowledge any plausible alternate interpretations (e.g., reverse causality, third variable) of the results from non-experimental studies (50.0%–88.9%). Moreover, most participants incorrectly selected a causal implication as following from the results of non-experimental studies (77.8%–100%). Our results suggest that even among authors of articles published in peer-review scientific journals on violence, many appear to infer causation from correlation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".