Work

Software application profile: tpc and micd—R packages for causal discovery with incomplete cohort data

Year: 2024

Type: article

Abstract: 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).

Source: International Journal of Epidemiology

DOI: 10.1093/ije/dyae113

Authors: Ryan M Andrews, Christine W Bang, Vanessa Didelez, Janine Witte, Ronja Foraita

Institutions: Boston University, Leibniz Institute for Prevention Research and Epidemiology - BIPS, University of Bremen

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Deutsche Forschungsgemeinschaft, National Institutes of Health, National Institute on Aging