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.
arXiv comment38 pages, 12 tables, 9 figures, submitted to Information and Software Technology
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- 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