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In this guide, we present how to perform constraint-based causal discoveryusing three popular software packages: pcalg (with add-ons tpc and micd),bnlearn, and TETRAD. We focus on how these packages can be used withobservational data and in the presence of mixed data (i.e., data where somevariables are continuous, while others are categorical), a known time orderingbetween variables, and missing data. Throughout, we point out the relativestrengths and limitations of each package, as well as give practicalrecommendations. We hope this guide helps anyone who is interested inperforming constraint-based causal discovery on their data.

A practical guide to causal discovery with cohort data