# Applications of statistical causal inference in software engineering

arXiv: https://arxiv.org/abs/2211.11482 · HTML: https://mlsanity.com/paper/2211.11482/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 127 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 30 of 31)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Causality-based Neural Network Repair](https://mlsanity.com/paper/2204.09274/) (2022)
- [Causal Fairness Analysis](https://mlsanity.com/paper/2207.11385/) (2022)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [From Verification to Causality-based Explications](https://mlsanity.com/paper/2105.09533/) (2021)
- [Unicorn: Reasoning about Configurable System Performance through the lens of Causality](https://mlsanity.com/paper/2201.08413/) (2022)
- [An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks](https://mlsanity.com/paper/2105.03092/) (2021)
- [Causality-Guided Adaptive Interventional Debugging](https://mlsanity.com/paper/2003.09539/) (2020)
- [Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques](https://mlsanity.com/paper/2102.06292/) (2021)
- [PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis](https://mlsanity.com/paper/2207.08369/) (2022)
- [Evolution of statistical analysis in empirical software engineering research: Current state and steps forward](https://mlsanity.com/paper/1706.00933/) (2017)
- [Testing Causality in Scientific Modelling Software](https://mlsanity.com/paper/2209.00357/) (2022)
- [A causal learning framework for the analysis and interpretation of COVID-19 clinical data](https://mlsanity.com/paper/2105.06998/) (2021)
- [Causal Modeling](https://mlsanity.com/paper/1303.1471/) (2013)
- [Inforence: Effective Fault Localization Based on Information-Theoretic Analysis and Statistical Causal Inference](https://mlsanity.com/paper/1712.03361/) (2017)
- [Computational Causal Inference](https://mlsanity.com/paper/2007.10979/) (2020)
- [An empirical study of Linespots: A novel past-fault algorithm](https://mlsanity.com/paper/2007.09394/) (2020)
- [Effectively Sampling Higher Order Mutants Using Causal Effect](https://mlsanity.com/paper/2104.11005/) (2021)
- [Bayesian propensity score matching in automotive embedded software engineering](https://mlsanity.com/paper/2109.12563/) (2021)
- [Causal inference for data-driven debugging and decision making in cloud computing](https://mlsanity.com/paper/1603.01581/) (2016)
- [ACDC: Altering Control Dependence Chains for Automated Patch Generation](https://mlsanity.com/paper/1705.00811/) (2017)
- [Causality Analysis for Concurrent Reactive Systems (Extended Abstract)](https://mlsanity.com/paper/1901.00589/) (2019)
- [Bayesian data analysis in empirical software engineering---The case of missing data](https://mlsanity.com/paper/1904.00661/) (2019)
- [Causal Program Dependence Analysis](https://mlsanity.com/paper/2104.09107/) (2021)
- [On the Use of Causal Graphical Models for Designing Experiments in the Automotive Domain](https://mlsanity.com/paper/2204.08743/) (2022)

## Built on by (most cited first, 12 of 12)

- [Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach](https://mlsanity.com/paper/2310.06680/) (2023)
- [DRCFS: Doubly Robust Causal Feature Selection](https://mlsanity.com/paper/2306.07024/) (2023)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [Dataflow graphs as complete causal graphs](https://mlsanity.com/paper/2303.09552/) (2023)
- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)
- [Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)](https://mlsanity.com/paper/2309.05264/) (2023)
- [Causal fault localisation in dataflow systems](https://mlsanity.com/paper/2304.11987/) (2023)
- [Your Assumed DAG is Wrong and Here's How To Deal With It](https://mlsanity.com/paper/2502.17030/) (2025)
- [CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models](https://mlsanity.com/paper/2308.01375/) (2023)
- [Causal Software Engineering: A Vision and Roadmap](https://mlsanity.com/paper/2605.02454/) (2026)
- [Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering](https://mlsanity.com/paper/2606.27960/) (2026)
- [The Complexity Kink: A Prompt-Side Structural Complexity Index for Code-Generation Reliability](https://mlsanity.com/paper/2609.19616/) (2026)

## Abstract (arXiv)

This paper reviews existing work in software engineering that applies statistical causal inference methods. These methods aim at estimating causal effects from observational data. The review covers 32 papers published between 2010 and 2022. Our results show that the application of statistical causal inference methods is relatively recent and that the corresponding research community remains relatively fragmented.