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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

```r
# run this once
install.packages("BiocManager")
BiocManager::install(c("graph","RBGL","Rgraphviz","ggm","pcalg"))

Install the development version from GitHub:

options(repos = c(CRAN = "https://cloud.r-project.org"))
# install.packages("devtools")
devtools::install_github("SamhitaPal3/PFCI")

library(PFCI)

sim <- simulate_pfci_toy()
fit <- pfci_fit(sim$X)
met <- pfci_metrics(sim, fit)
met

## $SHD
## [1] 34
## 
## $F1_total
## [1] 0.8365385
## 
## $MCC
## [1] 0.8336793
## 
## $Precision
## [1] 0.87
## 
## $Recall
## [1] 0.8055556
## 
## $TP
## [1] 87
## 
## $FP
## [1] 13
## 
## $FN
## [1] 21
## 
## $TN
## [1] 4829
## 
## $Time
## [1] 0.150439
## 
## $rho
## [1] 0.1617968

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. arXiv preprint arXiv:2507.00173.