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.

本文回顾了软件工程中应用统计因果推断方法的现有工作。回顾了 2010 年至 2022 年间发表的 32 篇论文。结果表明，统计因果推断方法的应用相对较新，相应的研究社区仍相对分散。

软件工程中的统计因果推断应用

Applications of statistical causal inference in software engineering

With the rapid development of computer software and hardware technologies,
more and more healthcare data are becoming readily available from clinical
institutions, patients, insurance companies and pharmaceutical industries,
among others. This access provides an unprecedented opportunity for data
science technologies to derive data-driven insights and improve the quality of
care delivery. Healthcare data, however, are usually fragmented and private
making it difficult to generate robust results across populations. For example,
different hospitals own the electronic health records (EHR) of different
patient populations and these records are difficult to share across hospitals
because of their sensitive nature. This creates a big barrier for developing
effective analytical approaches that are generalizable, which need diverse,
"big data". Federated learning, a mechanism of training a shared global model
with a central server while keeping all the sensitive data in local
institutions where the data belong, provides great promise to connect the
fragmented healthcare data sources with privacy-preservation. The goal of this
survey is to provide a review for federated learning technologies, particularly
within the biomedical space. In particular, we summarize the general solutions
to the statistical challenges, system challenges and privacy issues in
federated learning, and point out the implications and potentials in
healthcare.

通过联邦学习机制，能够保持本地机构中的敏感数据，同时在中央服务器上培训共享全球模型，从而改进卫生保健数据的隐私保护和数据分析，以解决医疗卫生数据碎片化和保密性问题。