mlsanityData to 14 Oct 2026

Dataflow graphs as complete causal graphs

arXiv 2303.09552 ↗First posted 16 Mar 2023cs.SE cs.AI cs.LGcited by 5

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Abstract (arXiv)

Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software design approaches make it difficult to track and discover such relationships at system scale, which leads to growing intellectual debt. In this paper we consider an alternative approach to software design, flow-based programming (FBP), and draw the attention of the community to the connection between dataflow graphs produced by FBP and structural causal models. With expository examples we show how this connection can be leveraged to improve day-to-day tasks in software projects, including fault localisation, business analysis and experimentation.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 10 of its 50 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

10 of 50 references.

References · 10

  1. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  2. 2020Challenges in Deploying Machine Learning: a Survey of Case Studiescited by 152
  3. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  4. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 41
  5. 2021Why did the distribution change?cited by 37
  6. 2022Applications of statistical causal inference in software engineeringcited by 12
  7. 2023Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARScited by 8
  8. 2022An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Contextcited by 5
  9. 2022Testing Causality in Scientific Modelling Softwarecited by 5
  10. 2022Desiderata for next generation of ML model servingcited by 5

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  2. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  3. 2024Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptationcited by 2
  4. 2025Probabilistic Modelling is Sufficient for Causal Inferencecited by 2
  5. 2023Causal fault localisation in dataflow systemscited by 1