# Automated Hyperparameter Selection for the PC Algorithm

arXiv: https://arxiv.org/abs/2011.01889 · HTML: https://mlsanity.com/paper/2011.01889/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice](https://mlsanity.com/paper/2310.18212/) (2023)
- [Learned Causal Method Prediction](https://mlsanity.com/paper/2311.03989/) (2023)

## Abstract (arXiv)

The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I α level. PC is however unsupervised, so we cannot tune α using traditional cross-validation. We therefore propose AutoPC, a fast procedure that optimizes α directly for a user chosen metric. We in particular force PC to double check its output by executing a second run on the recovered graph. We choose the final output as the one which maximizes stability between the two runs. AutoPC consistently outperforms the state of the art across multiple metrics.