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Using the discrete representation in the shared projection direction, causal relationships between heterogeneous non-Euclidean variables can be discovered more accurately. Finally, empirical research is conducted on real-world industrial sensor data, which demonstrates the effectiveness of the proposed method for discovering causal relationships in heterogeneous non-Euclidean data.<\/jats:p>","DOI":"10.1007\/s40747-024-01740-5","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T09:32:31Z","timestamp":1735983151000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Causal discovery and fault diagnosis based on mixed data types for system reliability modeling"],"prefix":"10.1007","volume":"11","author":[{"given":"Xiaokang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Siqi","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Xinghan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mozhu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"1740_CR1","unstructured":"L.\u00a0Y. 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