# CausIL: Causal Graph for Instance Level Microservice Data

arXiv: https://arxiv.org/abs/2303.00554 · HTML: https://mlsanity.com/paper/2303.00554/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2023.

## Built on by (most cited first, 9 of 9)

- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [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)
- [Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer](https://mlsanity.com/paper/2309.17340/) (2023)
- [OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies](https://mlsanity.com/paper/2510.13561/) (2025)
- [Effect-Level Validation for Causal Discovery](https://mlsanity.com/paper/2602.08340/) (2026)
- [EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis](https://mlsanity.com/paper/2607.27290/) (2026)
- [Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis](https://mlsanity.com/paper/2608.21310/) (2026)

## Abstract (arXiv)

AI-based monitoring has become crucial for cloud-based services due to its scale. A common approach to AI-based monitoring is to detect causal relationships among service components and build a causal graph. Availability of domain information makes cloud systems even better suited for such causal detection approaches. In modern cloud systems, however, auto-scalers dynamically change the number of microservice instances, and a load-balancer manages the load on each instance. This poses a challenge for off-the-shelf causal structure detection techniques as they neither incorporate the system architectural domain information nor provide a way to model distributed compute across varying numbers of service instances. To address this, we develop CausIL, which detects a causal structure among service metrics by considering compute distributed across dynamic instances and incorporating domain knowledge derived from system architecture. Towards the application in cloud systems, CausIL estimates a causal graph using instance-specific variations in performance metrics, modeling multiple instances of a service as independent, conditional on system assumptions. Simulation study shows the efficacy of CausIL over baselines by improving graph estimation accuracy by ~25% as measured by Structural Hamming Distance whereas the real-world dataset demonstrates CausIL's applicability in deployment settings.