# MRPC: An R package for accurate inference of causal graphs

arXiv: https://arxiv.org/abs/1806.01899 · HTML: https://mlsanity.com/paper/1806.01899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Conditional LoRA Parameter Generation](https://mlsanity.com/paper/2408.01415/) (2024)

## Abstract (arXiv)

We present MRPC, an R package that learns causal graphs with improved accuracy over existing packages, such as pcalg and bnlearn. Our algorithm builds on the powerful PC algorithm, the canonical algorithm in computer science for learning directed acyclic graphs. The improvement in accuracy results from online control of the false discovery rate (FDR) that reduces false positive edges, a more accurate approach to identifying v-structures (i.e., T1 rightarrow T2 leftarrow T3), and robust estimation of the correlation matrix among nodes. For genomic data that contain genotypes and gene expression for each sample, MRPC incorporates the principle of Mendelian randomization to orient the edges. Our package can be applied to continuous and discrete data.