ArticleEvolutionary human sciences2023
Exploring causality from observational data: An example assessing whether religiosity promotes cooperation.
Article in Evolutionary human sciences, 2023. 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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8 citing papers in PubMed.
- The longitudinal associations of material security and belief in God in young Americans.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
- Material insecurity and religiosity: A causal analysis.Evolutionary human sciences · 2024Article
- Identifying culture as cause: Challenges and opportunities.Evolutionary human sciences · 2024Article
- Releasing synthetic data from the Avon Longitudinal Study of Parents and Children (ALSPAC): Guidelines and applied examples.Wellcome open research · 2024Article
- Methods in causal inference. Part 1: causal diagrams and confounding.Evolutionary human sciences · 2024Article
- The world has gone mad.Evolutionary human sciences · 2024Article
- The associations between religious/spiritual beliefs and behaviours and study participation in a prospective cohort study (ALSPAC) in Southwest England.Wellcome open research · 2022Article
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Abstract
Causal inference from observational data is notoriously difficult, and relies upon many unverifiable assumptions, including no confounding or selection bias. Here, we demonstrate how to apply a range of sensitivity analyses to examine whether a causal interpretation from observational data may be justified. These methods include: testing different confounding structures (as the assumed confounding model may be incorrect), exploring potential residual confounding and assessing the impact of selection bias due to missing data. We aim to answer the causal question 'Does religiosity promote cooperative behaviour?' as a motivating example of how these methods can be applied. We use data from the parental generation of a large-scale (
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