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.