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Software application profile: tpc and micd-R packages for causal discovery with incomplete cohort data
Ryan M Andrews1,2, Christine W Bang2,3, Vanessa Didelez2,3
1Department of Epidemiology, Boston University, Boston, MA, USA.
Motivation:
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.
Implementation:
micd and tpc packages are R packages.
General Features:
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.
Availability:
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).
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