Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning
Abstract
Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Causal discovery extensively involves conditional independence (CI) tests. Hence, its output quality highly depends on the performance of CI tests, which can often be unreliable in practice. Moreover, privacy concerns arise when excessive CI tests are performed.
BibTeX
@inproceedings{Ma-al:ICSE24,
author = {Pingchuan Ma and
Zhenlan Ji and
Peisen Yao and
Shuai Wang and
Kui Ren},
title = {Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning},
booktitle = {ICSE},
pages = {30:1--30:13},
publisher = {{ACM}},
year = {2024},
}