A practical guide to causal discovery with cohort data
Applications
2023-12-20 v3
Abstract
In this guide, we present how to perform constraint-based causal discovery using three popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We focus on how these packages can be used with observational data and in the presence of mixed data (i.e., data where some variables are continuous, while others are categorical), a known time ordering between variables, and missing data. Throughout, we point out the relative strengths and limitations of each package, as well as give practical recommendations. We hope this guide helps anyone who is interested in performing constraint-based causal discovery on their data.
Cite
@article{arxiv.2108.13395,
title = {A practical guide to causal discovery with cohort data},
author = {Ryan M. Andrews and Ronja Foraita and Vanessa Didelez and Janine Witte},
journal= {arXiv preprint arXiv:2108.13395},
year = {2023}
}
Comments
27 pages, 20 figures