Janine Witte
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View article: Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure
Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure Open
Methods of causal discovery aim to identify causal structures in a data driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We…
View article: A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents
A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents Open
Childhood obesity is a complex disorder that appears to be influenced by an interacting system of many factors. Taking this complexity into account, we aim to investigate the causal structure underlying childhood obesity. Our focus is on i…
View article: Multiple imputation and test‐wise deletion for causal discovery with incomplete cohort data
Multiple imputation and test‐wise deletion for causal discovery with incomplete cohort data Open
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focusing on the causal relation between individual treatment‐outcome pairs. Constraint‐based causal discovery al…
View article: A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents
A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents Open
Childhood obesity is a complex disorder that appears to be influenced by an interacting system of many factors. Taking this complexity into account, we aim to investigate the causal structure underlying childhood obesity. Our focus is on i…
View article: Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data
Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data Open
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focussing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery a…
View article: A practical guide to causal discovery with cohort data
A practical guide to causal discovery with cohort data Open
In this guide, we present how to perform constraint-based causal discovery using three popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We focus on how these packages can be used with observational data an…
View article: On efficient adjustment in causal graphs
On efficient adjustment in causal graphs Open
We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustmen…
View article: On efficient adjustment in causal graphs
On efficient adjustment in causal graphs Open
We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustmen…