<p><b>A</b>: Graph showing a case where the statistical dependence between and is (partly) due to a causal interaction from to . <b>B</b>: Graph showing a case where the statistical dependence between and is induced solely by the confounding variable . <b>C</b>: Graph corresponding to the intervention in the causal graph shown in <b>A</b>. <b>D</b>: Graph corresponding to the intervention in the causal graph shown in <b>B</b>.</p
Many methods have been developed for inducing cause from statistical data. Those employing linear re...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
Recent data mining techniques exploit patterns of statistical independence in multivariate data to m...
Abstract: From their inception, causal systems models (more commonly known as structural-equations m...
⊳ So far: graphs as representation of probabilistic structure ∙ Dependencies and independencies of r...
<p>Boxes represent variables and arrows represent suggested causal links going from a cause to an ef...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are...
Heterogeneity and comorbidity are two interwoven challenges associated with various healthcare probl...
Observational studies often seek to estimate the causal relevance of an “exposure” to an “outcome” o...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
Following a long history of informal use in path analysis, causal diagrams (graphical causal models)...
BACKGROUND: Directed acyclic graphs (DAGs) are of great help when researchers try to understand the ...
Do-calculus is concerned with estimating the interventional distribution of an action from the obse...
Many methods have been developed for inducing cause from statistical data. Those employing linear re...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
Recent data mining techniques exploit patterns of statistical independence in multivariate data to m...
Abstract: From their inception, causal systems models (more commonly known as structural-equations m...
⊳ So far: graphs as representation of probabilistic structure ∙ Dependencies and independencies of r...
<p>Boxes represent variables and arrows represent suggested causal links going from a cause to an ef...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are...
Heterogeneity and comorbidity are two interwoven challenges associated with various healthcare probl...
Observational studies often seek to estimate the causal relevance of an “exposure” to an “outcome” o...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
Following a long history of informal use in path analysis, causal diagrams (graphical causal models)...
BACKGROUND: Directed acyclic graphs (DAGs) are of great help when researchers try to understand the ...
Do-calculus is concerned with estimating the interventional distribution of an action from the obse...
Many methods have been developed for inducing cause from statistical data. Those employing linear re...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
Recent data mining techniques exploit patterns of statistical independence in multivariate data to m...