Call for papers - Causal inference and observational data

Guest Editors:
Xiaoyu Liang: Michigan State University College of Human Medicine, USA
Ivan Olier: Liverpool John Moores University, UK
Victor Volovici: Erasmus University of Rotterdam, The Netherlands
Yiqiang Zhan: Karolinska Institutet, Sweden

Submission Status: Open | Submission Deadline: 6 December 2023

BMC Medical Research Methodology is 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 welcomes articles that address methodological challenges in using observational data to draw causal conclusions, with a focus on applications in medical and healthcare settings.

Submit to Collection

Meet the Guest Editors

Back to top

Xiaoyu Liang: Michigan State University College of Human Medicine, USA

Dr. Xiaoyu Liang’s research aims to better understand how genetic variation contributes to the etiology of complex diseases, and how genetic and environmental risks on substance abuse and other psychiatric disorders translate genetic findings to clinical use. Dr. Liang has a background in statistical genetics and epigenetics regulation in psychiatric disorders, with specific training and expertise in developing statistical methods and computational tools to map complex disease genes, drug and alcohol addiction and its relationship with HIV infection, joint analysis of multiple phenotypes in genome, epigenome, phenome-wide association studies, casual inference, and single‐cell RNA sequencing analysis.

Ivan Olier: Liverpool John Moores University, UK

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.

There are currently no articles in this collection.

Submission Guidelines

Back to top

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.