# CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications

arXiv: https://arxiv.org/abs/2209.02500 · HTML: https://mlsanity.com/paper/2209.02500/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2022.

## Built on by (most cited first, 24 of 24)

- [Root Cause Analysis In Microservice Using Neural Granger Causal Discovery](https://mlsanity.com/paper/2402.01140/) (2024)
- [GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?](https://mlsanity.com/paper/2508.12472/) (2025)
- [Robust Root Cause Diagnosis using In-Distribution Interventions](https://mlsanity.com/paper/2505.00930/) (2025)
- [Graph-Free Root Cause Analysis](https://mlsanity.com/paper/2601.21359/) (2026)
- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)
- [GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks](https://mlsanity.com/paper/2504.00058/) (2025)
- [Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning](https://mlsanity.com/paper/2605.04431/) (2026)
- [Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments](https://mlsanity.com/paper/2406.13604/) (2024)
- [CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems](https://mlsanity.com/paper/2406.19711/) (2024)
- [PORCA: Root Cause Analysis with Partially Observed Data](https://mlsanity.com/paper/2407.05869/) (2024)
- [Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?](https://mlsanity.com/paper/2501.02766/) (2025)
- [OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies](https://mlsanity.com/paper/2510.13561/) (2025)
- [Root Cause Analysis Method Based on Large Language Models with Residual Connection Structures](https://mlsanity.com/paper/2602.08804/) (2026)
- [EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems](https://mlsanity.com/paper/2605.00936/) (2026)
- [FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity](https://mlsanity.com/paper/2512.00844/) (2025)
- [Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems](https://mlsanity.com/paper/2602.12592/) (2026)
- [Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection](https://mlsanity.com/paper/2604.17998/) (2026)
- [STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices](https://mlsanity.com/paper/2604.26422/) (2026)
- [LATS-RCA: Language Agent Tree Search for Root Cause Analysis in Microservices](https://mlsanity.com/paper/2605.03505/) (2026)
- [StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis](https://mlsanity.com/paper/2606.05636/) (2026)
- [Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks](https://mlsanity.com/paper/2606.13532/) (2026)
- [NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks](https://mlsanity.com/paper/2606.13543/) (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)

## Abstract (arXiv)

Effectively localizing root causes of performance anomalies is crucial to enabling the rapid recovery and loss mitigation of microservice applications in the cloud. Depending on the granularity of the causes that can be localized, a service operator may take different actions, e.g., restarting or migrating services if only faulty services can be localized (namely, coarse-grained) or scaling resources if specific indicative metrics on the faulty service can be localized (namely, fine-grained). Prior research mainly focuses on coarse-grained faulty service localization, and there is now a growing interest in fine-grained root cause localization to identify faulty services and metrics. Causal inference (CI) based methods have gained popularity recently for root cause localization, but currently used CI methods have limitations, such as the linear causal relations assumption and strict data distribution requirements. To tackle these challenges, we propose a framework named CausalRCA to implement fine-grained, automated, and real-time root cause localization. The CausalRCA uses a gradient-based causal structure learning method to generate weighted causal graphs and a root cause inference method to localize root cause metrics. We conduct coarse- and fine-grained root cause localization to evaluate the localization performance of CausalRCA. Experimental results show that CausalRCA has significantly outperformed baseline methods in localization accuracy, e.g., the average AC@3 of the fine-grained root cause metric localization in the faulty service is 0.719, and the average increase is 10% compared with baseline methods. In addition, the average Avg@5 has improved by 9.43%.