ArticleInternational journal of data science and analytics2018
Comparison of strategies for scalable causal discovery of latent variable models from mixed data.
Article in International journal of data science and analytics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- The Physiology Of the WEight Reduced State (POWERS) study: overview and study design.International journal of obesity (2005) · 2026Review
- New causal discovery algorithm over censored variables identifies subtype-specific drivers of breast cancer progression.GigaScience · 2026Article
- Identifying pathways to cardiovascular mortality by causal graphical models and mediation analysis among hypertensive patients: insights from a prospective study.Journal of translational medicine · 2025Article
- Identifying Metabolomic Biomarkers of Lung Function Decline in People with HIV.Journal of acquired immune deficiency syndromes (1999) · 2025Article
- Investigating causal networks of dementia using causal discovery and natural language processing models.NPJ dementia · 2025Article
- Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.PLoS medicine · 2024Article
- A plasma peptidomic signature reveals extracellular matrix remodeling and predicts prognosis in alcohol-associated hepatitis.Hepatology communications · 2024Article
- Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure.Nature communications · 2024Article
- Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes.bioRxiv : the preprint server for biology · 2024Article
- Pre-vaccination transcriptomic profiles of immune responders to the MUC1 peptide vaccine for colon cancer prevention.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Pre-vaccination transcriptomic profiles of immune responders to the MUC1 peptide vaccine for colon cancer prevention.Frontiers in immunology · 2024Article
- Integrated BATF transcriptional network regulates suppressive intratumoral regulatory T cells.Science immunology · 2023Article
- Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes.BMC medicine · 2023Article
- Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis.Respiratory research · 2023Article
- Lipidomic signatures align with inflammatory patterns and outcomes in critical illness.Nature communications · 2022Article
- Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets.Patterns (New York, N.Y.) · 2022Article
- Causal Discovery in High-dimensional, Multicollinear Datasets.Frontiers in epidemiology · 2022Article
- Synthetic data generation with probabilistic Bayesian Networks.Mathematical biosciences and engineering : MBE · 2021Article
- Lipidomic Signatures Align with Inflammatory Patterns and Outcomes in Critical Illness.Research square · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data ("Mixed Data"), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inference algorithms can identify important relationships from biomedical data; however, handling the challenges of causal inference over mixed data with unmeasured confounders in a scalable way is still an open problem. Despite recent advances into causal discovery strategies that could potentially handle these challenges; individually, no study currently exists that comprehensively compares these approaches in this setting. In this paper, we present a comparative study that addresses this problem by comparing the accuracy and efficiency of different strategies in large, mixed datasets with latent confounders. We experiment with two extensions of the Fast Causal Inference algorithm: a maximum probability search procedure we recently developed to identify causal orientations more accurately, and a strategy which quickly eliminates unlikely adjacencies in order to achieve scalability to high-dimensional data. We demonstrate that these methods significantly outperform the state of the art in the field by achieving both accurate edge orientations and tractable running time in simulation experiments on datasets with up to 500 variables. Finally, we demonstrate the usability of the best performing approach on real data by applying it to a biomedical dataset of HIV-infected individuals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.