The DOI matches its Crossref record.
Root Cause Analysis of Failures in Microservices Through Causal Discovery
Overview
Integrity signals
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Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Metric-based RCA baselines include causal inference-based methods such as RUN, CausalRCA, CIRCA, RCD, MicroCause, EasyRCA, MSCRED, as well as noncausal methods such as BARO, and 𝜖-Diagnosis [6,8,10,14].”
From RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data (2025) · cites this paper as [8]
In the citing paper
“For example, existing studies [8,10,13] typically evaluate their methods on only 1-2 systems with 2-3 fault types.”
From RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data (2025) · cites this paper as [8]
In the citing paper
“Multi-source RCA baselines include PDiagnose, multi-source BARO, multi-source RCD, multi-source CIRCA [8,10,16].”
From RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data (2025) · cites this paper as [8]
Citations
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Cited by
Sorted by citations
RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data
Pham, L., Zhang, H., Ha, H., Salim, F., & Zhang, X. (2025) · Companion Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3701716.3715290 · Cited by 72
Xue, Y., Wang, Y., Zhu, A., Sun, X., & Zhang, C. (2026) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce69494.2026.11504875 · Cited by 13
PyRCA: A Library for Metric-based Root Cause Analysis
Liu, C., Yang, W., Mittal, H., Singh, M., Sahoo, D., & Hoi, S. C. H. (2023) · arXiv · doi:10.48550/arxiv.2306.11417 · Cited by 4
TORAI: Multi-source Root Cause Analysis for Blind Spots in Microservice Service Call Graph
Pham, L., Ha, H., Zhang, X., & Zhang, H. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3808137 · Cited by 10
CausalRCA: Causal Graph-Augmented Retrieval for Root Cause Analysis in Microservice Systems
Kou, J., Qing, Z., Wei, R., Xue, F., Huang, W., & Zhuang, H. (2026) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1109/eibdct69742.2026.11566729 · Cited by 2
Metadata
- Authors
- Ikram, A., Chakraborty, S., Mitra, S., Saini, S., Bagchi, S., & Kocaoglu, M.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 31158-31170
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133209642
- Cited by
- 35
- References
- 0
- Citations by year
- 2026: 35
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- root cause analysis, microservices, causal discovery, fault location, causal inference
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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