DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors

DAGSLAM provides a powerful and scalable tool for uncovering causal relationships in complex disease networks, with significant implications for risk factor identification and public health research.

Authors

Jinzhu Jia (ORCID: https://orcid.org/0000-0002-6554-2463)
Yuanyuan Zhao (ORCID: https://orcid.org/0000-0002-6107-2185)

Institutions

Peking University (CN)

Publication Details

Journal: BMC Medical Research Methodology
Published: 2025-06-06
DOI: https://doi.org/10.1186/s12874-025-02582-6
Citations: 1
Primary Topic: Bayesian Modeling and Causal Inference
Type: article
Field-Weighted Citation Impact: 2.48

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article

DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors

Jinzhu Jia, Yuanyuan Zhao

Jun 6, 2025

1 citations

BMC Medical Research Methodology

Bayesian Modeling and Causal Inference

2.48

article

DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors

Jinzhu Jia, Yuanyuan Zhao

June 6, 2025article en

1 citations

Abstract

DAGSLAM provides a powerful and scalable tool for uncovering causal relationships in complex disease networks, with significant implications for risk factor identification and public health research.

BMC Medical Research MethodologyVol. 25(1)

Peking University (CN)

Openalex Percentile: Top 10%

Bayesian Modeling and Causal Inference

2.48

DOI Open Access PDF OpenAlex

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DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors — Jinzhu Jia, Yuanyuan Zhao · BMC Medical Research Methodology (2025) | TGRS Research Map | TGRS