PORCA: Root Cause Analysis with Partially Observed Data

TL;DR

PORCA is a new framework for root cause analysis that effectively handles partial observations, unobserved confounders, and heterogeneity, improving reliability in diagnosing system faults.

Contribution

It introduces PORCA, a novel causal discovery framework that addresses partial observations and heterogeneity in root cause analysis, which were neglected in prior work.

Findings

Outperforms existing methods on synthetic and real datasets.

Effectively uncovers root causes despite missing data.

Demonstrates robustness to unobserved confounders.

Abstract

Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used in many application domains. Reliable diagnostic conclusions are of great importance in mitigating system failures and financial losses. However, previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation (i.e., missing nodes and latent malfunction). As a result, they fail in deriving reliable RCA results. In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data. To achieve this, we propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity. PORCA leverages magnified score-based causal discovery to efficiently optimize acyclic directed mixed graph under unobserved confounders. In addition, we also develop a heterogeneity-aware scheduling strategy to provide adaptive sample weights. Extensive experimental results on one synthetic and two real-world datasets demonstrate the effectiveness and superiority of the proposed framework.