Causal inference on human behaviour
Open accessAbstract
Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.
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Utilising and comparing findings from complementary approaches such as propensity-based matching or weighting and RI-CLPM can provide a form of triangulation of evidence and assist in illuminating biases from each approach.
“Utilising and comparing the findings from complementary approaches such as propensity‐based matching or weighting and RI‐CLPM can provide a form of triangulation of evidence and assist in the illumination of biases from each approach.”
From A counterfactual and random intercept cross‐lagged panel analysis of the effects of reading frequency on adolescent mental health in a large longitudinal study (2026) · cites this paper as Bailey et al., 2024
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- Authors
- Bailey, D. H., Jung, A. J., Beltz, A. M., Eronen, M. I., Gische, C., Hamaker, E. L., Kording, K. P., Lebel, C., Lindquist, M. A., Moeller, J., Razi, A., Rohrer, J. M., Zhang, B., & Murayama, K.
- Affiliations
- University of California, Irvine (US)
- University of Tübingen (DE)
- University of Michigan (US)
- University of Groningen (NL)
- Humboldt-Universität zu Berlin (DE)
- Utrecht University (NL)
- University of Pennsylvania (US)
- University of Calgary (CA)
- Johns Hopkins University (US)
- Leipzig University (DE)
- Canadian Institute for Advanced Research (CA)
- Syracuse University (US)
- Kochi University of Technology (JP)
- Corresponding author
- Bailey, Drew H.
- ORCID
- Venue
- Nature Human Behaviour
- Published
- 2024-08-23
- Volume
- 8
- Issue
- 8
- Pages
- 1448-1459
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W4401798123
- Cited by
- 56
- References
- 17
- Citations by year
- 2026: 25 · 2025: 27 · 2024: 4
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- The authors received funding from the Jacobs Foundation and CIFAR for a workshop on causal inference, which inspired this manuscript.
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Keywords
- causal inference, human behavior, causal language, confounding, heterogeneous effects, interference, complex treatment, research questions, triangulation, quasi-experimental design, observational data, theoretical assumptions
- MeSH terms
- Behavior, Humans, Research Design, Causality
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