Causal discoveries for high dimensional mixed data.
Causal relationships are of crucial importance for biological and medical research. Algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the no...
| Published in: | Statistics in Medicine Vol. 41; no. 24; pp. 4924 - 4941 |
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| Main Authors: | , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
10/30/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159688165&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159688165 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 10/30/2022 vid: 41 iid: 24 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159688165 158475360 159688165 NLM35968913 159688165 10.1002/sim.9544 NLM35968913 159688165 ppf: 4924 ppct: 17 formats: tig: atl: Causal discoveries for high dimensional mixed data. aug: au: Cai, Zhanrui Xi, Dong Zhu, Xuan Li, Runze affil: Department of Statistics, Iowa State University, Ames Iowa,, USA sug: subj: Algorithms Causal Attribution Statistics Computer Simulation Human ab: Causal relationships are of crucial importance for biological and medical research. Algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the normal distribution for all variables, challenges emerge from epidemiological and clinical studies where data are often mixed with continuous, binary, and ordinal variables. We propose to use a mixed latent Gaussian copula model to estimate the underlying correlation structure via the rank correlation for mixed data. This correlation structure is then incorporated into a popular causal discovery algorithm, the PC algorithm, to identify causal structures. The proposed algorithm, called the latent-PC algorithm, is able to discover the true causal structure consistently under mild conditions in high dimensional settings. From simulation studies, the latent-PC algorithm delivers a competitive performance in terms of a similar or higher true positive rate and a similar or lower false positive rate, compared with other variants of the PC algorithm. In the high dimensional settings where the number of variables is more than the number of observations, the causal graphs identified by the latent-PC algorithm are closer to the true causal structures, compared to other competing algorithms. Further, we demonstrate the utility of the latent-PC algorithm in a real dataset for hepatocellular carcinoma. Causal structures for patient survival are visualized and connected with clinical interpretations in the literature. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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