Evolutionary Human Sciences (Jan 2024)

Methods in causal inference. Part 1: causal diagrams and confounding

  • Joseph A. Bulbulia

DOI
https://doi.org/10.1017/ehs.2024.35
Journal volume & issue
Vol. 6

Abstract

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Causal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.

Keywords