Penalized Fast Causal Inference for High-Dimensional Structure Learning.

ReadMe

2026-03-14

PFCI: Penalized Fast Causal Inference for High-Dimensional Structure Learning

PFCI implements Penalized Fast Causal Inference (PFCI), a scalable two-stage procedure for learning graphical structures in high-dimensional settings with potential latent variables and selection bias.

The method combines:

• Graphical lasso screening to obtain a sparse super-skeleton
• Constrained Fast Causal Inference (FCI) for orientation and refinement

This enables computationally efficient structure learning while preserving theoretical guarantees under sparsity assumptions.

Installation

Install from CRAN:

install.packages("PFCI")

The development version is available on GitHub:

devtools::install_github("djghosh1123/PFCI")

Core functionality requires pcalg and graph from Bioconductor:

install.packages("BiocManager")
BiocManager::install(c("pcalg", "graph", "RBGL", "Rgraphviz"))

Basic usage

library(PFCI)

sim <- simulate_pfci_toy(p = 100, n = 100, edge_prob = 0.02, seed = 1)
fit <- pfci_fit(sim$X, alpha = 0.05)
met <- pfci_metrics(sim, fit)
met
plot_pag(fit)

Reference

Pal, S., Ghosh, D., & Yang, S. (2025). Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson’s Disease. Annals of Applied Statistics. doi:10.48550/arXiv.2507.00173