# Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?

arXiv: https://arxiv.org/abs/2408.13729 · HTML: https://mlsanity.com/paper/2408.13729/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2024.

## Built on by (most cited first, 15 of 15)

- [Root Cause Analysis of Outliers in Unknown Cyclic Graphs](https://mlsanity.com/paper/2510.06995/) (2025)
- [Graph-Free Root Cause Analysis](https://mlsanity.com/paper/2601.21359/) (2026)
- [PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis](https://mlsanity.com/paper/2512.22113/) (2025)
- [OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies](https://mlsanity.com/paper/2510.13561/) (2025)
- [SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions](https://mlsanity.com/paper/2604.03391/) (2026)
- [EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems](https://mlsanity.com/paper/2605.00936/) (2026)
- [What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarks](https://mlsanity.com/paper/2606.29159/) (2026)
- [Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation](https://mlsanity.com/paper/2508.01635/) (2025)
- [Agentic Diagnostic Reasoning over Telecom and Datacenter Infrastructure](https://mlsanity.com/paper/2601.07342/) (2026)
- [Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems](https://mlsanity.com/paper/2602.21928/) (2026)
- [Formalizing and falsifying causal pathways of rare events](https://mlsanity.com/paper/2605.31254/) (2026)
- [SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles](https://mlsanity.com/paper/2606.15559/) (2026)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)
- [Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models](https://mlsanity.com/paper/2609.36771/) (2026)
- [SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems](https://mlsanity.com/paper/2610.04893/) (2026)

## Abstract (arXiv)

Microservice architecture has become a popular architecture adopted by many cloud applications. However, identifying the root cause of a failure in microservice systems is still a challenging and time-consuming task. In recent years, researchers have introduced various causal inference-based root cause analysis methods to assist engineers in identifying the root causes. To gain a better understanding of the current status of causal inference-based root cause analysis techniques for microservice systems, we conduct a comprehensive evaluation of nine causal discovery methods and twenty-one root cause analysis methods. Our evaluation aims to understand both the effectiveness and efficiency of causal inference-based root cause analysis methods, as well as other factors that affect their performance. Our experimental results and analyses indicate that no method stands out in all situations; each method tends to either fall short in effectiveness, efficiency, or shows sensitivity to specific parameters. Notably, the performance of root cause analysis methods on synthetic datasets may not accurately reflect their performance in real systems. Indeed, there is still a large room for further improvement. Furthermore, we also suggest possible future work based on our findings.