# Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

arXiv: https://arxiv.org/abs/2402.01140 · HTML: https://mlsanity.com/paper/2402.01140/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems](https://mlsanity.com/paper/2103.01782/) (2021)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization](https://mlsanity.com/paper/2302.01987/) (2023)
- [Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings](https://mlsanity.com/paper/2108.00344/) (2021)

## Built on by (most cited first, 30 of 32)

- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models](https://mlsanity.com/paper/2509.11575/) (2025)
- [Root Cause Analysis of Outliers with Missing Structural Knowledge](https://mlsanity.com/paper/2406.05014/) (2024)
- [AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agents](https://mlsanity.com/paper/2504.06614/) (2025)
- [Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series](https://mlsanity.com/paper/2506.10412/) (2025)
- [Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2508.20370/) (2025)
- [Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://mlsanity.com/paper/2601.02732/) (2026)
- [GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?](https://mlsanity.com/paper/2508.12472/) (2025)
- [RuntimeSlicer: Towards Generalizable Unified Runtime State Representation for Failure Management](https://mlsanity.com/paper/2603.21495/) (2026)
- [Graph-Free Root Cause Analysis](https://mlsanity.com/paper/2601.21359/) (2026)
- [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)
- [StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems](https://mlsanity.com/paper/2606.03467/) (2026)
- [Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments](https://mlsanity.com/paper/2406.13604/) (2024)
- [PORCA: Root Cause Analysis with Partially Observed Data](https://mlsanity.com/paper/2407.05869/) (2024)
- [Time Series Domain Adaptation via Latent Invariant Causal Mechanism](https://mlsanity.com/paper/2502.16637/) (2025)
- [PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis](https://mlsanity.com/paper/2606.00582/) (2026)
- [Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks](https://mlsanity.com/paper/2408.08023/) (2024)
- [Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation](https://mlsanity.com/paper/2508.01635/) (2025)
- [Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory](https://mlsanity.com/paper/2510.09930/) (2025)
- [Causal explanations of outliers in systems with lagged time-dependencies](https://mlsanity.com/paper/2602.04667/) (2026)
- [Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems](https://mlsanity.com/paper/2602.21928/) (2026)
- [ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality](https://mlsanity.com/paper/2603.25538/) (2026)
- [Formalizing and falsifying causal pathways of rare events](https://mlsanity.com/paper/2605.31254/) (2026)
- [StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis](https://mlsanity.com/paper/2606.05636/) (2026)
- [Root Cause Analysis with Latent Confounders using Partial Ancestral Graphs](https://mlsanity.com/paper/2606.20912/) (2026)
- [KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI](https://mlsanity.com/paper/2607.01788/) (2026)
- [EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis](https://mlsanity.com/paper/2607.27290/) (2026)
- [OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning](https://mlsanity.com/paper/2608.00309/) (2026)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)

## Abstract (arXiv)

In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint the root cause due to the complex relationships in microservices when facing system malfunctions. Previous research employed structured learning methods (e.g., PC-algorithm) to establish causal relationships and derive root causes from causal graphs. Nevertheless, they ignored the temporal order of time series data and failed to leverage the rich information inherent in the temporal relationships. For instance, in cases where there is a sudden spike in CPU utilization, it can lead to an increase in latency for other microservices. However, in this scenario, the anomaly in CPU utilization occurs before the latency increase, rather than simultaneously. As a result, the PC-algorithm fails to capture such characteristics. To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning. RUN enhances the backbone encoder by integrating contextual information from time series, and leverages a time series forecasting model to conduct neural Granger causal discovery. In addition, RUN incorporates Pagerank with a personalization vector to efficiently recommend the top-k root causes. Extensive experiments conducted on the synthetic and real-world microservice-based datasets demonstrate that RUN noticeably outperforms the state-of-the-art root cause analysis methods. Moreover, we provide an analysis scenario for the sock-shop case to showcase the practicality and efficacy of RUN in microservice-based applications. Our code is publicly available at https://github.com/zmlin1998/RUN.