Causal inference and effect estimation using observational data

Who it's for: Complete beginners to causal inference who are looking for an easy-to-follow introductory guide.

Why we love it: Causal inference is a key part of systems thinking, serving as an important tool to understand causal relationships between different variables. This article provides a beginner's guide to interpreting and using causal inference literature from the field of economics and public health. The article provides an overview of the potential outcomes framework and directed acyclic graphs (i.e., DAGs or causal diagrams). It covers how to define and identify causal effects, with example diagrams, notations, and a section discussing bias. A perfect resource for beginners!

Citation: Igelström, E., Craig, P., Lewsey, J., Lynch, J., Pearce, A., & Katikireddi, S. V. (2022). Causal inference and effect estimation using observational data. J Epidemiol Community Health, 76(11), 960–966. https://doi.org/10.1136/jech-2022-219267