# Causal fault localisation in dataflow systems

arXiv: https://arxiv.org/abs/2304.11987 · HTML: https://mlsanity.com/paper/2304.11987/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Pathways: Asynchronous Distributed Dataflow for ML](https://mlsanity.com/paper/2203.12533/) (2022)
- [DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models](https://mlsanity.com/paper/2206.06821/) (2022)
- [Why did the distribution change?](https://mlsanity.com/paper/2102.13384/) (2021)
- [Monitoring and explainability of models in production](https://mlsanity.com/paper/2007.06299/) (2020)
- [Causal Testing: Finding Defects' Root Causes](https://mlsanity.com/paper/1809.06991/) (2018)
- [Causality in Configurable Software Systems](https://mlsanity.com/paper/2201.07280/) (2022)
- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques](https://mlsanity.com/paper/2102.06292/) (2021)
- [BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters](https://mlsanity.com/paper/2112.08774/) (2021)
- [An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context](https://mlsanity.com/paper/2204.12781/) (2022)
- [Desiderata for next generation of ML model serving](https://mlsanity.com/paper/2210.14665/) (2022)
- [Dataflow graphs as complete causal graphs](https://mlsanity.com/paper/2303.09552/) (2023)
- [A Large-Scale Study of Call Graph-based Impact Prediction using Mutation Testing](https://mlsanity.com/paper/1812.06286/) (2018)
- [Towards better data discovery and collection with flow-based programming](https://mlsanity.com/paper/2108.04105/) (2021)

## Built on by (most cited first, 1 of 1)

- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)

## 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.