cyclinbayes: Bayesian Causal Discovery with Linear Non-Gaussian Directed Acyclic and Cyclic Graphical Models

TL;DR

cyclinbayes is an R package that enables scalable Bayesian causal discovery for both acyclic and cyclic linear models, providing comprehensive uncertainty quantification and principled graph estimation methods.

Contribution

It introduces a Bayesian approach with spike-and-slab priors for causal discovery in linear non-Gaussian models, including cyclic graphs, with efficient algorithms and uncertainty quantification.

Findings

Provides posterior edge inclusion probabilities and graph motif probabilities.

Scales efficiently to large datasets with hybrid MCMC algorithms.

Offers a new decision-theoretic method for graph summarization.

Abstract

We introduce cyclinbayes, an open-source R package for discovering linear causal relationships with both acyclic and cyclic structures. The package employs scalable Bayesian approaches with spike-and-slab priors to learn directed acyclic graphs (DAGs) and directed cyclic graphs (DCGs) under non-Gaussian noise. A central feature of cyclinbayes is comprehensive uncertainty quantification, including posterior edge inclusion probabilities, posterior probabilities of network motifs, and posterior probabilities over entire graph structures. Our implementation addresses two limitations in existing software: (1) while methods for linear non-Gaussian DAG learning are available in R and Python, they generally lack proper uncertainty quantification, and (2) reliable implementations for linear non-Gaussian DCG remain scarce. The package implements computationally efficient hybrid MCMC algorithms that scale to large datasets. Beyond uncertainty quantification, we propose a new decision-theoretic approach to summarize posterior samples of graphs, yielding principled point estimates based on posterior expected loss such as posterior expected structural Hamming distance and structural intervention distance. The package, a supplementary material, and a tutorial are available on GitHub at https://github.com/roblee01/cyclinbayes.