{"title":"Commentary: The Life-Long Importance of Nutrition in the First 1000 Days","authors":"Reynaldo Martorell","doi":"10.1002/ajhb.70137","DOIUrl":"https://doi.org/10.1002/ajhb.70137","url":null,"abstract":"","PeriodicalId":50809,"journal":{"name":"American Journal of Human Biology","volume":"37 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elijah J. Watson, Delaney J. Glass, Lucia C. Petito
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.
{"title":"Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data","authors":"Elijah J. Watson, Delaney J. Glass, Lucia C. Petito","doi":"10.1002/ajhb.70149","DOIUrl":"https://doi.org/10.1002/ajhb.70149","url":null,"abstract":"<p>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.</p>","PeriodicalId":50809,"journal":{"name":"American Journal of Human Biology","volume":"37 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajhb.70149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}