The epidemiological concept of confounding has had a convoluted history. It was first expressed as an issue of group non-comparability, later as an uncontrolled fallacy, then as a controllable fallacy ...

School of Mathematics and System Sciences, Beihang University, Beijing, China. Causal inference has become an important research field in statistics, data mining, epidemiology and machine learning etc ...

Confounding of three binary-variable counterfactual model with directed acyclic graph (DAG) is discussed in this paper. According to the effect between the control variable and the covariate variable, ...

Objectives To adjust for confounding in observational data, researchers use propensity score matching (PSM), but more advanced methods might be required when dealing with longitudinal data and ...

We all follow the scientific method in one way or another, perhaps without even knowing it! How often have you become faced with a problem and then come up with a solution, only to have your plans ...

1 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box 14155-6446, Tehran, Iran 2 Department of Ophthalmology and Visual Sciences, ...

This repository contains a Jupyter notebook that explores the concept of confounding variables in causal inference. The notebook provides both theoretical explanations and practical coding examples to ...

Neurointervention is a highly specialized area of medicine and, as such, neurointerventional research studies are often more challenging to conduct, require large, multicenter efforts and longer study ...