Applications of statistical causal inference in software engineering
arXiv:2211.11482 · doi:10.1016/j.infsof.2023.107198
Abstract
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.
38 pages, 12 tables, 9 figures, submitted to Information and Software Technology
References in corpus (8)
- Causal Machine Learning: A Survey and Open Problems
- Inforence: Effective Fault Localization Based on Information-Theoretic Analysis and Statistical Causal Inference
- Causal Fairness Analysis
- Causal Program Dependence Analysis
- From Verification to Causality-based Explications
- Causality Analysis for Concurrent Reactive Systems (Extended Abstract)
- Bayesian causal inference in automotive software engineering and online evaluation
- A causal learning framework for the analysis and interpretation of COVID-19 clinical data
Cited by in corpus (5)
- Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions
- Causal Reasoning in Software Quality Assurance: A Systematic Review
- CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models
- Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Controlled Experiment
- Requirements Quality Research Artifacts: Recovery, Analysis, and Management Guideline