{
  "abstract": "Real-world longitudinal clinical datasets present major challenges for causal discovery due to mixed variable types, temporal dependencies, treatment-selection mechanisms, missingness, and patient heterogeneity. We extended the Causal Inference over Mixtures (CIM) framework to accommodate mixed-type longitudinal data and applied it to 1,937 hospitalized patients from the Khorshid COVID Cohort. The analysis integrated admission characteristics, symptom trajectories, in-hospital treatments, laboratory measures, and short-term outcomes up to 12 weeks after discharge using mixed conditional-independence testing, bootstrap stability selection, graph-based interpretation, and follow-up outcome assessment. The recovered ancestral structures revealed clinically coherent symptom clusters at admission and persistent associations among several post-discharge symptoms, including cough, fatigue/weakness, shortness of breath, weight loss, and loss of smell. Treatment-related associations were interpreted cautiously because cumulative treatment exposure may reflect disease severity, hospitalization duration, physician decision-making, and other selection mechanisms. In particular, the observed associations involving ceftriaxone and interferon-β should be regarded as exploratory and require independent validation. Simulation studies demonstrated that CIM consistently outperformed PC, FCI, RFCI, and CCI, achieving the highest recovery accuracy and balanced accuracy while maintaining low false-positive rates and competitive computational efficiency, even in the presence of latent confounding and selection bias. These results support the feasibility and utility of the proposed framework for identifying robust structural patterns in complex longitudinal clinical data. Given the observational nature of the study, the single-cohort design, and the absence of external replication, the findings should be viewed primarily as hypothesis-generating rather than confirmatory evidence of causal effects.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Statistics, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.",
    "Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kosar Abdollahi"
    },
    {
      "affs": [
        0
      ],
      "name": "Zahra Rezaei Ghahroodi",
      "orcid": "0000-0002-9138-8535"
    },
    {
      "affs": [
        1
      ],
      "name": "Marjan Mansourian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ae6ftd3pe2k09.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0355553",
  "fulltext_url": "https://europepmc.org/articles/PMC13722330",
  "id": "ae6ftd3pe2k09",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0355553",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Algorithms"
    ],
    [
      "heading",
      "Applied Mathematics"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Clinical Laboratories"
    ],
    [
      "heading",
      "Clinical Laboratory Sciences"
    ],
    [
      "heading",
      "Clinical Medicine"
    ],
    [
      "heading",
      "Computer and Information Sciences"
    ],
    [
      "heading",
      "Coughing"
    ],
    [
      "heading",
      "Covid 19"
    ],
    [
      "heading",
      "Diagnostic Medicine"
    ],
    [
      "heading",
      "Directed Acyclic Graphs"
    ],
    [
      "heading",
      "Directed Graphs"
    ],
    [
      "heading",
      "Drug Discovery"
    ],
    [
      "heading",
      "Drug Research and Development"
    ],
    [
      "heading",
      "Dyspnea"
    ],
    [
      "heading",
      "Graph Theory"
    ],
    [
      "heading",
      "Health Care"
    ],
    [
      "heading",
      "Health Care Facilities"
    ],
    [
      "heading",
      "Hospitals"
    ],
    [
      "heading",
      "Infectious Diseases"
    ],
    [
      "heading",
      "Information Theory"
    ],
    [
      "heading",
      "Mathematics"
    ],
    [
      "heading",
      "Medical Conditions"
    ],
    [
      "heading",
      "Medicine and Health Sciences"
    ],
    [
      "heading",
      "Pharmacology"
    ],
    [
      "heading",
      "Physical Sciences"
    ],
    [
      "heading",
      "Physiological Processes"
    ],
    [
      "heading",
      "Physiology"
    ],
    [
      "heading",
      "Pulmonology"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Research and Analysis Methods"
    ],
    [
      "heading",
      "Respiratory Disorders"
    ],
    [
      "heading",
      "Signs and Symptoms"
    ],
    [
      "heading",
      "Simulation and Modeling"
    ],
    [
      "heading",
      "Viral Diseases"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological processes/Coughing"
    ],
    [
      "plos",
      "/Computer and information sciences/Information theory/Graph theory/Directed graphs/Directed acyclic graphs"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Coughing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Clinical laboratory sciences/Clinical laboratories"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Health care facilities/Hospitals"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Viral diseases/COVID 19"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drug research and development/Drug discovery"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Mixtures"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Graph theory/Directed graphs/Directed acyclic graphs"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://europepmc.org/articles/PMC13722330?pdf=render",
  "pmcid": "PMC13722330",
  "pmid": "42961987",
  "published": "2026-10-30",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/ae6ftd3pe2k09.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:20:55Z",
      "native_id": "10.1371/journal.pone.0355553",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0355553"
    },
    {
      "first_seen": "2026-11-02T03:21:21Z",
      "native_id": "MED:42961987",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42961987"
    },
    {
      "first_seen": "2026-11-03T12:22:51Z",
      "native_id": "PMC13722330",
      "source": "pmc",
      "url": "https://doi.org/10.1371/journal.pone.0355553"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Biology and Life Sciences",
      "subfield": "general"
    },
    {
      "field": "chemistry",
      "origin": "plos:drug discovery",
      "subfield": "medicinal"
    },
    {
      "field": "mathematics",
      "origin": "heading:Mathematics",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "plos:infectious diseases",
      "subfield": "infectious-disease"
    },
    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "Causal discovery and inference in COVID-19: A multiscale longitudinal analysis using mixtures of DAGs",
  "title_text": "Causal discovery and inference in COVID-19: A multiscale longitudinal analysis using mixtures of DAGs",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:51Z",
  "url": "https://research.draconitic.com/paper/ae6ftd3pe2k09",
  "venue": "PLOS One",
  "versions": []
}