A Review on Algorithms for Constraint-based Causal Discovery
This is an incremental review for researchers in data mining and causal inference, summarizing existing approaches without introducing new methods.
The paper reviews constraint-based causal discovery algorithms, which mine causal relationships from observational data, and surveys state-of-the-art methods, software packages, and benchmark repositories.
Causal discovery studies the problem of mining causal relationships between variables from data, which is of primary interest in science. During the past decades, significant amount of progresses have been made toward this fundamental data mining paradigm. Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency. In this paper, we aim to review the constraint-based causal discovery algorithms. Firstly, we discuss the learning paradigm of the constraint-based approaches. Secondly and primarily, the state-of-the-art constraint-based casual inference algorithms are surveyed with the detailed analysis. Thirdly, several related open-source software packages and benchmark data repositories are briefly summarized. As a conclusion, some open problems in constraint-based causal discovery are outlined for future research.