

@article{andrews2023practicalguidecausaldiscovery,
    author = {Andrews, Ryan M and Bang, Christine W and Didelez, Vanessa and Witte, Janine and Foraita, Ronja},
    title = {Software application profile: tpc and micd—R packages for causal discovery with incomplete cohort data},
    journal = {International Journal of Epidemiology},
    volume = {53},
    number = {5},
    pages = {dyae113},
    year = {2024},
    month = {08},
    abstract = {The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc, fill these gaps.micd and tpc packages are R packages.The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors.The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd; https://github.com/bips-hb/tpc).},
    issn = {1464-3685},
    url = {https://doi.org/10.1093/ije/dyae113},
}