Establishing causal inferences from experimental and observational data: A critical review and primer for sport and exercise psychology
Bibliographic record
Abstract
Causal inference is a central goal of research in sport and exercise psychology. However, current norms in sport and exercise psychology privilege experimental designs over observational designs for deriving causal inferences. Causal inferences can be derived from either observational or experimental designs, given that the causal effect of interest is clearly specified, and particular assumptions are met. Drawing from contemporary theory and evidence in the causal inference domain, the purpose of this review is to promote broader thinking around causal inference in sport and exercise psychology. In addition to providing an overview of the systematic process of causal inference, guidance will be provided on the assumptions that underpin principled causal inference of mechanisms (i.e., mediation) from both experimental and observational designs, using examples tailored to the interests of sport and exercise psychology researchers.
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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 | 0.099 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".