Causal inference on human behaviour

Drew H. Bailey*, Alexander J. Jung, Adriene M. Beltz, Markus I. Eronen, Christian Gische, Ellen L. Hamaker, Konrad P. Kording, Catherine Lebel, Martin A. Lindquist, Julia Moeller, Adeel Razi, Julia M. Rohrer, Baobao Zhang, Kou Murayama

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1448-1459
Number of pages12
JournalNature Human Behaviour
Volume8
Issue number8
DOIs
Publication statusPublished - Aug 2024

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