Distribution-free root cause analysis

TL;DR

This paper introduces a distribution-free framework called Conformal Root Cause Analysis (CROC) for identifying the earliest change point in multiple data streams, providing valid confidence sets under minimal assumptions.

Contribution

The paper proposes CROC, a novel conformal p-value-based method for root cause analysis that offers finite-sample valid confidence sets and extends to dependent streams.

Findings

CROC accurately localizes the root stream in simulations.

The method provides finite-sample valid confidence sets.

CROC extends to handle cross-stream dependence.

Abstract

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.