# Causal Link Discovery with Unequal Edge Error Tolerance

arXiv: https://arxiv.org/abs/2507.21570 · HTML: https://mlsanity.com/paper/2507.21570/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression](https://mlsanity.com/paper/2608.17132/) (2026)
- [Statistical Testing on Directed Graphs by Surrogate Data Generation](https://mlsanity.com/paper/2606.00758/) (2026)
- [On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models](https://mlsanity.com/paper/2609.17942/) (2026)

## Abstract (arXiv)

This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where different types of edge errors may have different importance, current state-of-the-art causal discovery algorithms do not differentiate between the types of edge errors, nor provide any finite-sample guarantees on the edge errors. Hence, this framework seeks to minimize one type of error while keeping the other below a user-specified tolerance level. Using techniques from information theory, fundamental performance limits are found, characterized by the R'enyi divergence, for Neyman-Pearson causal discovery. Furthermore, a causal discovery algorithm is introduced for the case of linear additive Gaussian noise models, called epsilon-CUT, that provides finite-sample guarantees on the false positive rate, while staying competitive with state-of-the-art methods.