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Causal inference and observational data

Guest Editors:
Ivan Olier: Liverpool John Moores University, UK
Victor Volovici: Erasmus University of Rotterdam, The Netherlands
Yiqiang Zhan: Karolinska Institutet, Sweden


BMC Medical Research Methodology was calling for submissions to our Collection on "Causal inference and observational data".

Causal inference is an essential area of study with major importance across disciplines. Allowing researchers to identify the factors leading to specific outcomes, causal inference in the field of medical research, can potentially inform medical practice and health policies, in turn improving public health outcomes. For instance, it can provide insights into the underlying mechanisms of disease and illness, help evaluate the effectiveness of public health policies and interventions, and address ethical considerations in research.

Observational studies are common sources of data for causal inference. Causal inference can be made using statistical models that separate causal effects from spurious correlations. Because observational studies are subject to bias and confounding, careful study design and adequate statistical methods are needed to ensure that the drawn conclusions are valid.

This collection welcomed articles that address methodological challenges in using observational data to draw causal conclusions, with a focus on applications in medical and healthcare settings.

Meet the Guest Editors

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Dr. Olier is a Senior Lecturer in Data Science and Leader of the Machine Learning Research Group at Liverpool John Moores University (UK). With more than 80 peer-reviewed manuscripts in AI and related fields, he has an extensive track record of over 15 years in the development of explainable AI algorithms for risk prediction modelling in healthcare and trustworthy AI. The usual application domains of his research in AI are health, pharmacy, and bioinformatics.

Victor Volovici: Erasmus University of Rotterdam, The Netherlands

Dr. Victor Volovici obtained his PhD in neurosurgery and epidemiology at the Erasmus University of Rotterdam, The Netherlands.  His research focuses on stroke, particularly hemorrhagic stroke, and on applying new methodological techniques for causal inference to answer critical research questions arising during clinical practice. While being a firm supporter of the power of randomized controlled trials, in some cases (diseases with low prevalence, or heterogeneity of treatment definitions- e.g. surgical treatment) observational techniques should be employed to inform best practice. He aims to bring advanced statistical techniques closer to clinicians and make them more understandable and feasible to use. He further enjoys a partnership and a collaboration with the "Iuliu Hatieganu" University, Cluj-Napoca, Romania, where he served as a visiting professor of experimental microsurgery, participating and leading microsurgical skill acquisition and skill maintenance research.

Yiqiang Zhan: Karolinska Institutet, Sweden

Dr. Yiqiang Zhan is a researcher in epidemiology and biostatistics at Karolinska Institutet and Sun Yat-Sen University. He has been working on aging epidemiology using novel causal inference techniques and study designs including instrumental variable approach, twin study design, and parametric survival analysis methods since his PhD studies. His current research focuses on the aetiology of neurodegenerative disorders by applying genetic and non-genetic analytic approaches to large-scale observational data collected from nationwide surveys and regional health registers.

About the collection

Causal inference is an essential area of study with major importance across disciplines. Allowing researchers to identify the factors leading to specific outcomes, causal inference in the field of medical research, can potentially inform medical practice and health policies, in turn improving public health outcomes. For instance, it can provide insights into the underlying mechanisms of disease and illness, help evaluate the effectiveness of public health policies and interventions, and address ethical considerations in research.

Observational studies are common sources of data for causal inference. Causal inference can be made using statistical models that separate causal effects from spurious correlations. Because observational studies are subject to bias and confounding, careful study design and adequate statistical methods are needed to ensure that the drawn conclusions are valid.

This collection welcomes articles that address methodological challenges in using observational data to draw causal conclusions, with a focus on applications in medical and healthcare settings. Topics of interest include, but are not limited to: 

•    Development, evaluation, or comparison of methods for causal inference using observational data; 
•    Approaches for dealing with sources of bias in observational studies; 
•    Strategies for using big data statistics to improve causal inference from observational data; 
•    Applications of causal inference to specific areas of medicine and healthcare, such as epidemiology, public health, and clinical research.

Image credit: © alphaspirit / stock.adobe.com

  1. Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodol...

    Authors: Heather Hufstedler, Nicole Mauer, Edmund Yeboah, Sinclair Carr, Sabahat Rahman, Alexander M. Danzer, Thomas P. A. Debray, Valentijn M.T. de Jong, Harlan Campbell, Paul Gustafson, Lauren Maxwell, Thomas Jaenisch, Ellicott C. Matthay and Till Bärnighausen
    Citation: BMC Medical Research Methodology 2024 24:91
    Content type: Research Published on:

  3. In the causal mediation analysis framework, several parametric regression-based approaches have been introduced in past years for decomposing the total effect of an exposure on a binary outcome into a direct e...

    Authors: Miguel Caubet, Kevin L’Espérance, Anita Koushik and Geneviève Lefebvre
    Citation: BMC Medical Research Methodology 2024 24:72
    Content type: Research Published on:

  4. Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect hete...

    Authors: John M. Brooks, Cole G. Chapman, Brian K. Chen, Sarah B. Floyd and Neset Hikmet
    Citation: BMC Medical Research Methodology 2024 24:66
    Content type: Research Published on:

  5. Eurotransplant liver transplant candidates are prioritized by Model for End-stage Liver Disease (MELD), a 90-day waitlist survival risk score based on the INR, creatinine and bilirubin. Several studies revised...

    Authors: H. C. de Ferrante, M. van Rosmalen, B. M. L. Smeulders, S. Vogelaar and F. C. R. Spieksma
    Citation: BMC Medical Research Methodology 2024 24:51
    Content type: Research Published on:

  6. Mendelian randomization is a popular method for causal inference with observational data that uses genetic variants as instrumental variables. Similarly to a randomized trial, a standard Mendelian randomizatio...

    Authors: Haodong Tian, Brian D. M. Tom and Stephen Burgess
    Citation: BMC Medical Research Methodology 2024 24:34
    Content type: Research Published on:

  7. Causal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data ...

    Authors: Marjan Meurisse, Francisco Estupiñán-Romero, Javier González-Galindo, Natalia Martínez-Lizaga, Santiago Royo-Sierra, Simon Saldner, Lorenz Dolanski-Aghamanoukjan, Alexander Degelsegger-Marquez, Stian Soiland-Reyes, Nina Van Goethem and Enrique Bernal-Delgado
    Citation: BMC Medical Research Methodology 2023 23:248
    Content type: Research Published on:

  8. When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each...

    Authors: Chen Huang, Kecheng Wei, Ce Wang, Yongfu Yu and Guoyou Qin
    Citation: BMC Medical Research Methodology 2023 23:233
    Content type: Research Published on:

  9. In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the avera...

    Authors: Ce Wang, Kecheng Wei, Chen Huang, Yongfu Yu and Guoyou Qin
    Citation: BMC Medical Research Methodology 2023 23:231
    Content type: Research Published on:

  10. 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 unravelin...

    Authors: Ivan Olier, Yiqiang Zhan, Xiaoyu Liang and Victor Volovici
    Citation: BMC Medical Research Methodology 2023 23:227
    Content type: Editorial Published on:

Submission Guidelines

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This Collection welcomes submission of Research articles, Database articles, and Software articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ["Causal Inference and Observational Data"] from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.