Cargando…
DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference
The current paradigm for causal inference in epidemiology relies primarily on the evaluation of counterfactual contrasts via statistical regression models informed by graphical causal models (often in the form of directed acyclic graphs, or DAGs) and their underlying mathematical theory. However, th...
Autores principales: | Arnold, Kellyn F, Harrison, Wendy J, Heppenstall, Alison J, Gilthorpe, Mark S |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380300/ https://www.ncbi.nlm.nih.gov/pubmed/30520989 http://dx.doi.org/10.1093/ije/dyy260 |
Ejemplares similares
-
A causal inference perspective on the analysis of compositional data
por: Arnold, Kellyn F, et al.
Publicado: (2020) -
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
por: Tennant, Peter W G, et al.
Publicado: (2020) -
Analyses of ‘change scores’ do not estimate causal effects in observational data
por: Tennant, Peter W G, et al.
Publicado: (2021) -
Equivalence model: A new graphical model for causal inference
por: Poorolajal, Jalal
Publicado: (2020) -
Adjustment for energy intake in nutritional research: a causal inference perspective
por: Tomova, Georgia D, et al.
Publicado: (2021)