“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 Murayama2024-08-23 (, , ; similar)⁠:

Making causal inferences regarding human behavior is difficult given the complex interplay between countless contributors to behavior, 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 behavior.

The challenges include our ambiguous causal language and thinking, statistical under-control/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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Causal Inference on Human Behaviour