ArticleEvolutionary human sciences2024
Methods in causal inference. Part 3: measurement error and external validity threats.
Article in Evolutionary human sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- How work hours affect well-being: A target trial emulation.PloS one · 2026Article
- Target trial emulation shows that supported causal effects of religious attendance on well-being are selective.Evolutionary human sciences · 2026Article
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Culturally Diverse Perceptions of EEG and Neurofeedback Research and How to Address Them to Reduce Sampling Bias.Psychophysiology · 2025Article
- Requiem of Olympic ethics and sports' independence: A panel-data socio-cultural analysis.PloS one · 2025Article
- Methods in causal inference. Part 4: confounding in experiments.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 2: Interaction, mediation, and time-varying treatments.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 1: causal diagrams and confounding.Evolutionary human sciences · 2024Article
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Abstract
The human sciences should seek generalisations wherever possible. For ethical and scientific reasons, it is desirable to sample more broadly than 'Western, educated, industrialised, rich, and democratic' (WEIRD) societies. However, restricting the target population is sometimes necessary; for example, young children should not be recruited for studies on elderly care. Under which conditions is unrestricted sampling desirable or undesirable? Here, we use causal diagrams to clarify the structural features of measurement error bias and target population restriction bias (or 'selection restriction'), focusing on threats to valid causal inference that arise in comparative cultural research. We define any study exhibiting such biases, or confounding biases, as weird (wrongly estimated inferences owing to inappropriate restriction and distortion). We explain why statistical tests such as configural, metric and scalar invariance cannot address the structural biases of weird studies. Overall, we examine how the workflows for causal inference provide the necessary preflight checklists for ambitious, effective and safe comparative cultural research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.