ParallelPC: an R package for efficient constraint based causal exploration

TL;DR

ParallelPC is an R package that significantly accelerates constraint-based causal discovery algorithms using parallel computing, making it feasible to analyze high-dimensional datasets on standard multicore computers.

Contribution

This paper introduces ParallelPC, the first R package to parallelize multiple causal discovery algorithms, reducing computational time and memory usage for high-dimensional data analysis.

Findings

Speeds up causal discovery on large datasets

Enables analysis on standard multicore computers

Reduces memory consumption during computation

Abstract

Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complexity, which hinders their applications in real world high dimensional datasets, e.g gene expression datasets. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. The parallelised algorithms help speed up the procedure of experimenting big datasets and reduce the memory used when running the algorithms. The package is not only suitable for super-computers or clusters, but also convenient for researchers using personal computers with multi core CPUs. Our experiment results on real world datasets show that using the parallelised algorithms it is now practical to explore causal relationships in high dimensional datasets with thousands of variables in a single multicore computer. ParallelPC is available in CRAN repository at https://cran.rproject.org/web/packages/ParallelPC/index.html.