Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data
Bibliographic record
Abstract
Human biologists seek to understand how cultural, environmental, and biological forces shape observed patterns of human variation. Yet contemporary insights and approaches to observational causal inference remain underutilized in the field. We outline a structured but flexible roadmap for causal inference in human biology that begins with theory development, defines causal questions and estimands, employs directed acyclic graphs (DAGs) to clarify assumptions, and evaluates key identification criteria prior to statistical analysis. We position this framework within a spectrum of causal inference traditions, spanning from interventionist approaches rooted in well-defined, manipulable exposures to realized approaches that engage historically situated and ecologically embedded phenomena. Rather than offering a prescriptive checklist, we frame this toolkit as an opening: a step toward anthropological causal inference that integrates transparency, theoretical and methodological coherence, and the epistemological commitments of the biocultural synthesis in human biology and anthropology.
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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 | 0.227 | 0.212 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.005 | 0.072 |
| Scholarly communication | 0.018 | 0.053 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.009 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".