{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:42:17Z","timestamp":1740109337759,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms, yet it remains a highly challenging problem due to dimensionality and complex relationships presented in data. In this study, we introduce LCIT (Latent representation-based Conditional Independence Test)\u2014a novel method for conditional independence testing based on representation learning. Our main contribution involves a hypothesis testing framework in which to test for the independence between <jats:italic>X<\/jats:italic> and <jats:italic>Y<\/jats:italic> given <jats:italic>Z<\/jats:italic>, we first learn to infer the latent representations of target variables <jats:italic>X<\/jats:italic> and <jats:italic>Y<\/jats:italic> that contain no information about the conditioning variable <jats:italic>Z<\/jats:italic>. The latent variables are then investigated for any significant remaining dependencies, which can be performed using a conventional correlation test. Moreover, LCIT can also handle discrete and mixed-type data in general by converting discrete variables into the continuous domain via variational dequantization. The empirical evaluations show that LCIT outperforms several state-of-the-art baselines consistently under different evaluation metrics, and is able to adapt really well to both nonlinear, high-dimensional, and mixed data settings on a diverse collection of synthetic and real data sets.\n<\/jats:p>","DOI":"10.1007\/s10115-023-01964-w","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T16:04:21Z","timestamp":1693238661000},"page":"357-380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Normalizing flows for conditional independence testing"],"prefix":"10.1007","volume":"66","author":[{"given":"Bao","family":"Duong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thin","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"key":"1964_CR1","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1177\/089443939100900106","volume":"9","author":"P Spirtes","year":"1991","unstructured":"Spirtes P, Glymour C (1991) An algorithm for fast recovery of sparse causal graphs. 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