Many epidemiological studies seek to assess the effect of one or several exposures on one or more outcomes. However, to quantify the causal inference produced, statistical techniques are commonly used that contrast the association among the variables of interest, not precisely of causal effect.(1) In fact, although these measures may not have a causal interpretation, the results are often adjusted for all potential confounding factors. (2,3) Some contemporary epidemiologists developed new methodological tools for causal inference,like the theory or contra-factual model(4) and representation of causal effects through the Directed Acyclic Graph (DAG).(5) The DAG, a fusion of the probability theory with trajectory diagrams, is quite useful to ...
Following a long history of informal use in path analysis, causal diagrams (graphical causal models)...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questi...
Epidemiologists typically seek to answer causal questions using statistical data:we observe a statis...
Abstract: From their inception, causal systems models (more commonly known as structural-equations m...
Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine b...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Abstract Methods of diagrammatic modelling have been greatly developed in the past two decades. Outs...
Observational studies often seek to estimate the causal relevance of an “exposure” to an “outcome” o...
The goal of most epidemiological studies is to determine an unbiased estimate of the effect of being...
Compartmental model diagrams have been used for nearly a century to depict causal relationships in i...
Following a long history of informal use in path analysis, causal diagrams (graphical causal models)...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questi...
Epidemiologists typically seek to answer causal questions using statistical data:we observe a statis...
Abstract: From their inception, causal systems models (more commonly known as structural-equations m...
Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine b...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
In respiratory health research, interest often lies in estimating the effect of an exposure on a hea...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Abstract Methods of diagrammatic modelling have been greatly developed in the past two decades. Outs...
Observational studies often seek to estimate the causal relevance of an “exposure” to an “outcome” o...
The goal of most epidemiological studies is to determine an unbiased estimate of the effect of being...
Compartmental model diagrams have been used for nearly a century to depict causal relationships in i...
Following a long history of informal use in path analysis, causal diagrams (graphical causal models)...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questi...