mlsanityData to 7 Oct 2026

Causal fault localisation in dataflow systems

arXiv 2304.11987 ↗First posted 24 Apr 2023cs.SE cs.AIcited by 1

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Abstract (arXiv)

Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic features of dataflow computing is the natural access to the dataflow graph of the entire system. Recently it has been observed that these dataflow graphs can be treated as complete graphical causal models, opening opportunities to apply causal inference techniques to dataflow systems. In this demonstration paper we aim to provide the first practical validation of this idea with a particular focus on causal fault localisation. We provide multiple demonstrations of how causal inference can be used to detect software bugs and data shifts in multiple scenarios with three modern dataflow engines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 24 Apr 2023.

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14 of 26 references.

References · 14

  1. 2022Pathways: Asynchronous Distributed Dataflow for MLcited by 81
  2. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
  3. 2021Why did the distribution change?cited by 37
  4. 2020Monitoring and explainability of models in productioncited by 23
  5. 2018Causal Testing: Finding Defects' Root Causescited by 22
  6. 2022Causality in Configurable Software Systemscited by 13
  7. 2022Applications of statistical causal inference in software engineeringcited by 12
  8. 2021Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniquescited by 8
  9. 2021BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameterscited by 6
  10. 2022An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Contextcited by 5
  11. 2022Desiderata for next generation of ML model servingcited by 5
  12. 2023Dataflow graphs as complete causal graphscited by 5
2 more
  1. 2018A Large-Scale Study of Call Graph-based Impact Prediction using Mutation Testingcited by 3
  2. 2021Towards better data discovery and collection with flow-based programmingcited by 3

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  1. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3