Causal inference and observational data

被引：0

作者：

Ivan Olier

Yiqiang Zhan

Xiaoyu Liang

Victor Volovici

机构：

[1] Liverpool John Moores University,Data Science Research Centre

[2] Karolinska Institutet,Institute of Environmental Medicine

[3] Michigan State University College of Human Medicine,Department of Epidemiology and Biostatistics

[4] Department of Neurosurgery,undefined

[5] Center for Medical Decision Making,undefined

[6] Erasmus MC,undefined

来源：

BMC Medical Research Methodology | / 23卷

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D O I：

暂无

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摘要：

Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences, and other fields. However, challenges like evaluating models and bias amplification remain.

引用

共 50 条

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Zhan, Yiqiang
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[J]. BMC MEDICAL RESEARCH METHODOLOGY, 2023, 23 (01)
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[J]. PSYCHOLOGICAL MEDICINE, 2021, 51 (04) : 563 - 578

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