PFCI: Penalized Fast Causal Inference for High-Dimensional Structure
Learning
Implements Penalized Fast Causal Inference (PFCI), a two-stage
causal structure learning procedure for high-dimensional settings with
potential latent variables and selection bias. In the first stage,
neighborhood selection via the Lasso constructs a sparse undirected
skeleton. In the second stage, the Fast Causal Inference (FCI) algorithm
orients edges on this reduced graph, producing a Partial Ancestral Graph
(PAG) that accounts for latent confounders. The method is consistent
under sparsity assumptions and substantially faster than standard FCI
and RFCI in high dimensions. See Pal, Ghosh, and Yang (2025)
<doi:10.48550/arXiv.2507.00173> for the underlying theory.
| Version: |
0.1.1 |
| Imports: |
stats, glasso, methods |
| Suggests: |
pcalg, graph, RBGL, Rgraphviz, testthat (≥ 3.0.0), knitr, rmarkdown, spelling |
| Published: |
2026-06-03 |
| DOI: |
10.32614/CRAN.package.PFCI |
| Author: |
Samhita Pal  [aut],
Dhrubajyoti Ghosh
[aut, cre],
Shu Yang [aut] |
| Maintainer: |
Dhrubajyoti Ghosh <dghosh3 at kennesaw.edu> |
| BugReports: |
https://github.com/djghosh1123/PFCI/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/djghosh1123/PFCI |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
PFCI citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
PFCI results |
Documentation:
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