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arxiv: 2605.27022 · v1 · pith:BDGU6G24new · submitted 2026-05-26 · 💻 cs.AI

ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis

Phi Nguyen Xuan , Nicholas Tagliapietra , Lavdim Halilaj , Kristian Kersting , Juergen Luettin This is my paper

Pith reviewed 2026-06-29 16:38 UTC · model grok-4.3

classification 💻 cs.AI
keywords causal analysisroot cause analysismulti-agent systemsinteractive copilotcausal discoverycausal effect estimationexplainability
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The pith

ORCA orchestrates agents to guide users through causal analysis workflows from automatic to user-guided.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ORCA as an end-to-end interactive copilot for causal analysis tasks. It aims to bridge the gap between complex causal methods and domain experts who lack the training to apply them directly in areas like manufacturing and medicine. ORCA achieves this by using agents that interpret user goals and select suitable workflows covering causal discovery, effect estimation, explainability, and root cause analysis. The system further supports users by evaluating methods, producing metrics and diagrams, and delivering structured reports. If effective, this would let non-specialists obtain reliable causal insights without needing to master the underlying techniques.

Core claim

ORCA orchestrates agents to understand the user's goals and guide them through the most appropriate causal analysis workflow, from fully automatic to highly user-guided execution. It features causal discovery, causal effect estimation, explainability and Root-Cause-Analysis (RCA). ORCA evaluates and compares performance, generates key metrics and diagrams, and generates insights through structured reports.

What carries the argument

Multi-agent orchestration that interprets user goals and selects and executes the appropriate causal workflow.

If this is right

  • Users gain access to both fully automatic execution and highly interactive guidance within one system.
  • The copilot produces performance evaluations and comparisons across causal methods.
  • Structured reports with metrics and diagrams communicate findings to non-experts.
  • Effectiveness is demonstrated on several real-world use cases across domains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same orchestration pattern could be adapted to guide users through other families of analytical methods beyond causal inference.
  • Interactive mode might surface and correct modeling choices that pure automation would miss on noisy industrial data.
  • If the agent layer generalizes, similar copilots could lower the barrier for validating new causal techniques when real data access is restricted.

Load-bearing premise

The multi-agent orchestration can reliably choose and run valid causal methods on real data without introducing errors or biases.

What would settle it

Apply ORCA to a dataset with known ground-truth causal structure and check whether its selected workflow and final root-cause report recover the correct relationships.

Figures

Figures reproduced from arXiv: 2605.27022 by Juergen Luettin, Kristian Kersting, Lavdim Halilaj, Nicholas Tagliapietra, Phi Nguyen Xuan.

Figure 1
Figure 1. Figure 1: ORCA is an assistant for Causal Analysis. By interacting with the user and its provided data and information (left), it orchestrates the execution of the most appropriate workflow for a specific causal analysis task. It features a wide variety of methods (center) and generates reports tailored to the user needs (right). Req. 6 Algorithmic Recommendation and Automation: The system must provide access to sta… view at source ↗
Figure 2
Figure 2. Figure 2: Multi-agent architecture illustrating the workflow management backbone (middle) that orchestrates the whole workflow with user interaction (top) and executing required individual agents (bottom). framework to streamline the interaction across various integrated modules; and 3) Generated Output - responsible for providing the results in various formats and visualization options. 4.1. System Architecture The… view at source ↗
Figure 3
Figure 3. Figure 3: Scenarios. Example of ORCA functionalities in different use-cases: a) Manufacturing, and b) Retail. discount-rate effect its profit-margin. Validating different factors with this model, the company can decide on the most effective measures to maximize their profit. 3. Manufacturing: A manufacturing company assembling magnetic valves and hydraulic blocks to hydraulic units is detecting increased leakage fai… view at source ↗
read the original abstract

Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptual and methodological complexity of causal methods makes them largely inaccessible to domain experts. This gap prevents experts from leveraging these advances and hinders researchers who lack access to real-world data for validation. To bridge this divide, we introduce ORCA, a copilot for end-to-end causal analysis. ORCA orchestrates agents to understand the user's goals and guide them through the most appropriate causal analysis workflow, from fully automatic to highly user-guided execution. It features causal discovery, causal effect estimation, explainability and Root-Cause-Analysis (RCA). ORCA evaluates and compares performance, generates key metrics and diagrams, and generates insights through structured reports. We highlight its effectiveness across several real-world use-cases.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces ORCA, an end-to-end interactive copilot for root cause analysis that orchestrates multiple agents to interpret user goals and guide execution of causal workflows ranging from fully automatic to highly user-guided. The system incorporates causal discovery, causal effect estimation, explainability, and RCA, while also performing performance evaluation and comparison, generating metrics and diagrams, and producing structured insight reports. The authors assert effectiveness across several real-world use-cases in domains such as manufacturing, social science, and medicine.

Significance. If the multi-agent orchestration and workflow execution were shown to reliably produce accurate causal results without introducing selection errors or biases, ORCA could meaningfully lower the barrier for domain experts to apply causal methods, enabling broader use of discovery, effect estimation, and RCA on real-world data where methodological expertise is limited.

major comments (2)
  1. [Abstract] Abstract: The claim that the system 'highlight[s] its effectiveness across several real-world use-cases' is unsupported, as the manuscript supplies no description of the use-cases, datasets, evaluation metrics, validation procedures, error analysis, or comparisons against non-agent baselines.
  2. [Abstract] Abstract: The central assumption that multi-agent orchestration 'reliably selects and executes valid causal workflows' without agent-induced errors or biases is untestable, because no architecture details, decision rules for workflow selection, or safeguards are provided.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each of the two major comments below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the system 'highlight[s] its effectiveness across several real-world use-cases' is unsupported, as the manuscript supplies no description of the use-cases, datasets, evaluation metrics, validation procedures, error analysis, or comparisons against non-agent baselines.

    Authors: We agree that the abstract claim is unsupported by the details listed. We will revise the abstract to remove this sentence. We will also add a new evaluation section to the manuscript body that describes the use-cases, datasets, metrics, validation procedures, error analysis, and comparisons against non-agent baselines. revision: yes

  2. Referee: [Abstract] Abstract: The central assumption that multi-agent orchestration 'reliably selects and executes valid causal workflows' without agent-induced errors or biases is untestable, because no architecture details, decision rules for workflow selection, or safeguards are provided.

    Authors: We acknowledge that the manuscript currently lacks the architecture details, decision rules, and safeguards needed to evaluate this assumption. We will add a dedicated section describing the multi-agent architecture, the workflow selection logic, and any safeguards against selection errors or biases. revision: yes

Circularity Check

0 steps flagged

No circularity: system description without derivations or fitted predictions

full rationale

The paper is a high-level description of an interactive multi-agent copilot system for causal analysis workflows. It contains no equations, no parameter fitting, no 'predictions' of derived quantities, and no load-bearing self-citations that reduce a central claim to an unverified prior result by the same authors. The contribution is architectural and descriptive; effectiveness is asserted via real-world use-cases without any mathematical derivation chain that could be circular. This matches the default non-circular case for system papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no technical content from which free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.1-grok · 5684 in / 1051 out tokens · 44267 ms · 2026-06-29T16:38:12.498517+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

63 extracted references · 20 canonical work pages · 2 internal anchors

  1. [1]

    Spirtes, C

    P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction, and Search, 2 ed., MIT Press, Cambridge, MA, 2000

    2000

  2. [2]

    K. M. Kellogg, Z. Hettinger, M. Shah, R. L. Wears, C. R. Sellers, M. Squires, R. J. Fairbanks, Our current approach to root cause analysis: is it contributing to our failure to improve patient safety?, BMJ Quality & Safety 26 (2017) 381–387

    2017

  3. [3]

    M. C. F. Prosperi, Y. Guo, M. Sperrin, J. S. Koopman, J. Min, X. He, S. N. Rich, M. Wang, I. E. Buchan, J. Bian, Causal inference and counterfactual prediction in machine learning for actionable healthcare, Nature Machine Intelligence 2 (2020) 369 – 375

    2020

  4. [4]

    A. W. Wu, A. K. M. Lipshutz, P. J. Pronovost, Effectiveness and efficiency of root cause analysis in medicine, Journal of the American Medical Association 299 (2008) 685–687

    2008

  5. [5]

    Imbens, Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics, NBER Working Paper Series (2019)

    G. Imbens, Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics, NBER Working Paper Series (2019)

    2019

  6. [6]

    Kleinberg, G

    S. Kleinberg, G. Hripcsak, A review of causal inference for biomedical informatics, Journal of biomedical informatics 44 6 (2011) 1102–12

    2011

  7. [7]

    J. Li, B. B. Chu, I. F. Scheller, J. Gagneur, M. H. Maathuis, Root cause discovery via permutations and cholesky decomposition, Journal of the Royal Statistical Society Series B: Statistical Methodology (2025)

    2025

  8. [8]

    Glymour, K

    C. Glymour, K. Zhang, P. Spirtes, Review of causal discovery methods based on graphical models, Frontiers in Genetics 10 (2019)

    2019

  9. [9]

    e Oliveira, V

    E. e Oliveira, V. L. Miguéis, J. L. Borges, Automatic root cause analysis in manufacturing: an overview & conceptualization, Journal of Intelligent Manufacturing 34 (2022) 2061–2078

    2022

  10. [10]

    Papageorgiou, T

    K. Papageorgiou, T. Theodosiou, A. Rapti, E. I. Papageorgiou, N. Dimitriou, D. Tzovaras, G. Margetis, A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing, Frontiers in Manufacturing Technology 2 (2022) 972712

    2022

  11. [11]

    M. Solé, V. Muntés-Mulero, A. I. Rana, G. Estrada, Survey on models and techniques for root-cause analysis, arXiv:1701.08546 (2017).arXiv:1701.08546

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  12. [12]

    Soldani, A

    J. Soldani, A. Brogi, Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey, ACM Computing Surveys 55 (2022)

    2022

  13. [13]

    A. L. Cochrane, Effectiveness and efficiency: random reflections on health services (1972)

    1972

  14. [14]

    Pearl, Causality, 2 ed., Cambridge University Press, 2009

    J. Pearl, Causality, 2 ed., Cambridge University Press, 2009

    2009

  15. [15]

    Cheng, R

    L. Cheng, R. Guo, R. Moraffah, P. Sheth, K. S. Candan, H. Liu, Evaluation methods and measures for causal learning algorithms, IEEE Transactions on Artificial Intelligence 3 (2022) 924–943

    2022

  16. [16]

    Gentzel, D

    A. Gentzel, D. Garant, D. Jensen, The case for evaluating causal models using interventional measures and empirical data, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, volume 32, Curran Associates, Inc., 2019

    2019

  17. [17]

    W. R. Orchard, N. Okati, S. H. G. Mejia, P. Blöbaum, D. Janzing, Root cause analysis of outliers with missing structural knowledge, in: The Annual Conference on Neural Information Processing Systems, 2025

    2025

  18. [18]

    E. S. Saveliev, T. Schubert, T. Pouplin, V. Kosmoliaptsis, M. van der Schaar, Climb: An ai-enabled partner for clinical predictive modeling, ArXiv abs/2410.03736 (2024)

    work page arXiv 2024

  19. [19]

    M. J. Vowels, N. C. Camgoz, R. Bowden, D’ya like dags? a survey on structure learning and causal discovery, ACM Computing Surveys 55 (2021) 1 – 36

    2021

  20. [20]

    A. R. Nogueira, A. Pugnana, S. Ruggieri, D. Pedreschi, J. Gama, Methods and tools for causal discovery and causal inference, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12 (2022)

    2022

  21. [21]

    Zecevic, M

    M. Zecevic, M. Willig, D. S. Dhami, K. Kersting, Causal parrots: Large language models may talk causality but are not causal, ArXiv abs/2308.13067 (2023)

    work page arXiv 2023

  22. [22]

    Z. Jin, J. Liu, Z. Lyu, S. Poff, M. Sachan, R. Mihalcea, M. T. Diab, B. Scholkopf, Can large language models infer causation from correlation?, ArXiv abs/2306.05836 (2023)

    work page arXiv 2023

  23. [23]

    Kıcıman, R

    E. Kıcıman, R. O. Ness, A. Sharma, C. Tan, Causal reasoning and large language models: Opening a new frontier for causality, ArXiv abs/2305.00050 (2023)

    work page arXiv 2023

  24. [24]

    Efficient Causal Graph Discovery Using Large Language Models

    T. Jiralerspong, X. Chen, Y. More, V. Shah, Y. Bengio, Efficient causal graph discovery using large language models, ArXiv abs/2402.01207 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  25. [25]

    Hasan, M

    U. Hasan, M. O. Gani, Optimizing data-driven causal discovery using knowledge-guided search,

  26. [26]

    URL: https://arxiv.org/abs/2304.05493.arXiv:2304.05493

    work page arXiv

  27. [27]

    C. Liu, Y. Chen, T. Liu, M. Gong, J. Cheng, B. Han, K. Zhang, Discovery of the hidden world with large language models, ArXiv abs/2402.03941 (2024)

    work page arXiv 2024

  28. [28]

    H. Du, Y. Zheng, B. Jing, Y. Zhao, G. Kou, G. Liu, T. Gu, W. Li, C. Yang, Causal discovery through synergizing large language model and data-driven reasoning, in: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, KDD ’25, Association for Computing Machinery, New York, NY, USA, 2025, p. 543–554. URL: https://doi.org/...

    work page doi:10.1145/3711896 2025

  29. [29]

    T. Ban, L. Chen, D. Lyu, X. Wang, Q. Zhu, H. Chen, Llm-driven causal discovery via harmonized prior, IEEE Transactions on Knowledge and Data Engineering 37 (2025) 1943–1960

    2025

  30. [30]

    Vashishtha, A

    A. Vashishtha, A. Kumar, A. Pandey, A. G. Reddy, K. Ahuja, V. N. Balasubramanian, A. Sharma, Teaching transformers causal reasoning through axiomatic training, 2025. URL: https://arxiv.org/ abs/2407.07612.arXiv:2407.07612

    work page arXiv 2025

  31. [31]

    Antonucci, G

    A. Antonucci, G. Piqué, M. Zaffalon, Zero-shot causal graph extrapolation from text via llms, 2023. URL: https://arxiv.org/abs/2312.14670.arXiv:2312.14670

    work page arXiv 2023

  32. [32]

    Saveliev, J

    E. Saveliev, J. Liu, N. Seedat, A. Boyd, M. van der Schaar, Towards human-guided, data-centric llm co-pilots, 2025. URL: https://arxiv.org/abs/2501.10321.arXiv:2501.10321

    work page arXiv 2025

  33. [33]

    Berrevoets, J

    J. Berrevoets, J. Piskorz, R. Davis, H. Amad, J. Weatherall, M. van der Schaar, Technical report: Facilitating the adoption of causal inference methods through llm-empowered co-pilot, 2025. URL: https://arxiv.org/abs/2508.10581.arXiv:2508.10581

    work page arXiv 2025

  34. [34]

    X. Wang, K. Zhou, W. Wu, H. S. Singh, F. Nan, S. Jin, A. Philip, S. Patnaik, H. Zhu, S. Singh, P. P. Prashant, Q. Shen, B. Huang, Causal-copilot: An autonomous causal analysis agent, ArXiv abs/2504.13263 (2025)

    work page arXiv 2025

  35. [35]

    A. Shan, J. Kaur, R. Singh, T. Banka, R. Yavatkar, T. Sridhar, Rca copilot: Transforming network data into actionable insights via large language models, ICC 2025 - IEEE International Conference on Communications (2025) 1566–1571

    2025

  36. [36]

    C. W. J. Granger, Investigating causal relations by econometric models and cross-spectral methods, 1969

    1969

  37. [37]

    Shyalika, A

    C. Shyalika, A. Sharma, F. E. Kalach, U. Jaimini, C. Henson, R. F. Harik, A. P. Sheth, Causaltrace: A neurosymbolic causal analysis agent for smart manufacturing, ArXiv abs/2510.12033 (2025)

    work page arXiv 2025

  38. [38]

    Shyalika, R

    C. Shyalika, R. Prasad, A. T. A. Ghazo, D. Eswaramoorthi, S. S. Muthuselvam, A. P. Sheth, Smartpilot: Agent-based copilot for intelligent manufacturing, in: Adaptive Agents and Multi-Agent Systems, 2025

    2025

  39. [39]

    Verma, S

    V. Verma, S. Acharya, S. Simko, D. Bhardwaj, A. Haghighat, M. Sachan, D. Janzing, B. Schölkopf, Z. Jin, Causal ai scientist: Facilitating causal data science with large language models, 2025

    2025

  40. [40]

    J. L. Gamella, J. Peters, P. Bühlmann, Causal chambers as a real-world physical testbed for AI methodology, Nature Machine Intelligence (2025)

    2025

  41. [41]

    Tagliapietra, J

    N. Tagliapietra, J. Luettin, L. Halilaj, M. Willig, T. Pychynski, K. Kersting, Causalman: A physics- based simulator for large-scale causality, arXiv:2502.12707 (2025).arXiv:2502.12707

    work page arXiv 2025

  42. [42]

    Hardt, W

    M. Hardt, W. R. Orchard, P. Blöbaum, S. Kasiviswanathan, E. Kirschbaum, The petshop dataset – finding causes of performance issues across microservices, 2024.arXiv:2311.04806

    work page arXiv 2024

  43. [43]

    Ikram, S

    A. Ikram, S. Chakraborty, S. Mitra, S. K. Saini, S. Bagchi, M. Kocaoglu, Root cause analysis of failures in microservices through causal discovery, in: Neural Inf. Processing Systems, 2022

    2022

  44. [44]

    Dawoud, S

    A. Dawoud, S. Talupula, Prorca: A causal python package for actionable root cause analysis in real-world business scenarios, arXiv:2503.01475 (2025).arXiv:2503.01475

    work page arXiv 2025

  45. [45]

    Erdös, A

    P. Erdös, A. Rényi, On random graphs i, Publicationes Mathematicae Debrecen 6 (1959) 290

    1959

  46. [46]

    Barabási, R

    A.-L. Barabási, R. Albert, Emergence of scaling in random networks, Science 286 (1999) 509–512

    1999

  47. [47]

    Spirtes, C

    P. Spirtes, C. Meek, T. S. Richardson, Causal inference in the presence of latent variables and selection bias, in: Conference on Uncertainty in Artificial Intelligence, 1995

    1995

  48. [48]

    D. M. Chickering, Optimal structure identification with greedy search, J. Mach. Learn. Res. 3 (2002) 507–554

    2002

  49. [49]

    Nazaret, D

    A. Nazaret, D. Blei, Extremely greedy equivalence search, in: Proceedings of Conference on Uncertainty in Artificial Intelligence, 2024

    2024

  50. [50]

    yin Lam, B

    W. yin Lam, B. Andrews, J. Ramsey, Greedy relaxations of the sparsest permutation algorithm, ArXiv abs/2206.05421 (2022)

    work page arXiv 2022

  51. [51]

    Zheng, B

    X. Zheng, B. Aragam, P. K. Ravikumar, E. P. Xing, Dags with no tears: Continuous optimization for structure learning, in: Advances in Neural Information Processing Systems, 2018

    2018

  52. [52]

    I. Ng, A. Ghassami, K. Zhang, On the role of sparsity and dag constraints for learning linear dags,

  53. [53]

    URL: https://arxiv.org/abs/2006.10201.arXiv:2006.10201

    work page arXiv 2006

  54. [54]

    X. Wang, Y. Du, S. Zhu, L. Ke, Z. Chen, J. Hao, J. Wang, Ordering-based causal discovery with reinforcement learning, in: International Joint Conference on Artificial Intelligence, 2021

    2021

  55. [55]

    Shimizu, P

    S. Shimizu, P. O. Hoyer, A. Hyvarinen, A. Kerminen, A linear non-gaussian acyclic model for causal discovery, Journal of Machine Learning Research 7 (2006) 2003–2030

    2006

  56. [56]

    P. O. Hoyer, D. Janzing, J. M. Mooij, J. Peters, B. Scholkopf, Nonlinear causal discovery with additive noise models, in: Neural Information Processing Systems, 2008

    2008

  57. [57]

    Zhang, A

    K. Zhang, A. Hyvärinen, On the identifiability of the post-nonlinear causal model, in: Conference on Uncertainty in Artificial Intelligence, 2009

    2009

  58. [58]

    Tagliapietra, G

    N. Tagliapietra, G. L. Marchioni, M. Willig, J. Luettin, L. Halilaj, K. Kersting, Causalsteward: An agentic divide-conquer-combine copilot for causal discovery, 2026. URL: https://openreview.net/ forum?id=3lFAyPa9Fe

    2026

  59. [59]

    Runge, P

    J. Runge, P. J. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances 5 (2017)

    2017

  60. [60]

    A. Tank, I. Covert, N. J. Foti, A. Shojaie, E. B. Fox, Neural granger causality, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2021) 4267–4279

    2021

  61. [61]

    D. Liu, C. He, X. Peng, F. Lin, C. Zhang, S. Gong, Z. Li, J. Ou, Z. Wu, Microhecl: High-efficient root cause localization in large-scale microservice systems, in: IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, 2021, pp. 338–347

    2021

  62. [62]

    M. Li, Z. Li, K. Yin, X. Nie, W. Zhang, K. Sui, D. Pei, Causal inference-based root cause analysis for online service systems with intervention recognition, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)

    2022

  63. [63]

    Budhathoki, L

    K. Budhathoki, L. Minorics, P. Blöbaum, D. Janzing, Causal structure-based root cause analysis of outliers, in: International conference on machine learning, PMLR, 2022, pp. 2357–2369

    2022