# Distribution-free root cause analysis

arXiv: https://arxiv.org/abs/2605.21627 · HTML: https://mlsanity.com/paper/2605.21627/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 11 of 11)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Testing for Outliers with Conformal p-values](https://mlsanity.com/paper/2104.08279/) (2021)
- [Selection by Prediction with Conformal p-values](https://mlsanity.com/paper/2210.01408/) (2022)
- [Survey on Models and Techniques for Root-Cause Analysis](https://mlsanity.com/paper/1701.08546/) (2017)
- [Multiscale Change-Point Inference](https://mlsanity.com/paper/1301.7212/) (2013)
- [Optimal Change-Point Detection and Localization](https://mlsanity.com/paper/2010.11470/) (2020)
- [COCA: Generative Root Cause Analysis for Distributed Systems with Code Knowledge](https://mlsanity.com/paper/2503.23051/) (2025)
- [Offline changepoint localization using a matrix of conformal p-values](https://mlsanity.com/paper/2505.00292/) (2025)
- [Post-detection inference for sequential changepoint localization](https://mlsanity.com/paper/2502.06096/) (2025)
- [Conditional Testing based on Localized Conformal p-values](https://mlsanity.com/paper/2409.16829/) (2024)
- [Bootstrap confidence intervals for multiple change points based on moving sum procedures](https://mlsanity.com/paper/2106.12844/) (2021)

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

- [Distribution-free inference on the number of changepoints](https://mlsanity.com/paper/2609.08234/) (2026)

## Abstract (arXiv)

We study distribution-free root cause analysis in multi-stream data, where an evolving underlying system is observed through multiple data streams that may each undergo distributional changes at unknown timepoints. In such settings, the stream exhibiting the earliest change provides a natural starting point for investigating the underlying cause, which we refer to as the root-cause index. Leveraging conformal p-values, we propose a novel framework, Conformal Root Cause Analysis (CROC), which constructs finite-sample valid confidence sets for the root-cause index under minimal assumptions: the data streams are independent, and within each stream the pre- and post-change observations are sampled exchangeably from arbitrary and unknown distributions. We further establish a universality property, showing that any distribution-free method for root cause localization can be represented within the CROC framework. In addition, under mild regularity conditions and principled score design, our method yields asymptotically sharp confidence sets that efficiently isolate the root cause. We further extend CROC to efficiently handle cross-stream dependence when present. Extensive simulations demonstrate accurate localization of the root stream, supporting our theoretical guarantees.