Causal Directed Acyclic Graphs (DAGs) are like chainsaws: used well, they slice through heavy tasks; used poorly, and you might lose a limb. Here's the first of four tutorials in @Journal_EHS for human scientists new to causal inference: cambridge.org/core/journals/… Tips: - Understand that causal inference involves a larger workflow that starts by stating a clear causal question & target population. - Understand that larger workflow. E.g. consider you must also address positivity & causal consistency assumptions. - Your DAG is a tool to clarify unmeasured confounding, not a paintbrush for describing all reality. - Group common causes, let the spatial arrangement reflect chronological order. - Like time's arrow, ensure your DAG doesn't loop back. - When in doubt, present multiple DAGs. - There's lots of other material I reference in the article. I got my start from @_MiguelHernan's course, which is *free* here: edx.org/learn/data-analysis/… In later posts, I'll describe my other tutorials and why I'm so passionate about causal inference. The full special issue with lots of great work is here. Thanks to @EffersonCharles @tavitonst for driving it: cambridge.org/core/journals/… #CausalInference #DAGs