Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, Kui Ren

TL;DR
This paper introduces CICheck, a runtime verification tool that improves causal discovery by verifying the consistency of conditional independence statements using SMT solving, enhancing reliability and privacy.
Contribution
It presents a novel, sound, and efficient approach to verify and prune CI statements in causal discovery, addressing both reliability and privacy issues.
Findings
CICheck effectively detects erroneous CI statements.
CICheck prunes excessive CI statements to protect privacy.
The tool employs a four-stage decision procedure with optimizations.
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. Despite the distinct nature between unreliable and excessive CI tests, this paper identifies a unified and principled approach to addressing both of them. Generally, CI statements, the outputs of CI tests, adhere to Pearl's axioms, which are a set of well-established integrity constraints on conditional independence. Hence, we can either detect erroneous CI statements if they violate Pearl's axioms or prune excessive CI statements if they are logically…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Software Testing and Debugging Techniques · Bayesian Modeling and Causal Inference
