Traditionally, statistics has been viewed as the branch of science which deals with association. Many epidemiological research questions, however, are concerned with causation, not association. In this thesis we develop novel statistical methodology to address four epidemiological problems properly, from a causal inference point of view. We show, that for these four problems, our methods offer an attractive alternative to the `standard' methodology, which may not yield the desired (causal) inference
The causes of a disease have not been clarified sufficiently. For example, there may be no one who d...
Discute-se a base de construção do conceito de risco, a partir da descrição do modelo de inferência ...
Causal inference based on a restricted version of the potential outcomes approach reasoning is assum...
One of the more challenging issues in epidemiological research is being able to provide an unbiased ...
This chapter explores the idea that causal inference is warranted if and only if the mechanism under...
This book compiles and presents new developments in statistical causal inference. The accompanying d...
This chapter explores the idea that causal inference is warranted if and only if the mechanism under...
Epidemiologists typically seek to answer causal questions using statistical data:we observe a statis...
Inferring causality is necessary to achieve the goal of epidemiology, which is to elucidate the caus...
Increasingly, modern epidemiology has adopted complex causal frameworks incorporating individual- an...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Re...
Social factors are associated with a wide variety of health outcomes. Social epidemiology has succes...
There has been much debate about the relative emphasis of the field of epidemiology on causal infere...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
The causes of a disease have not been clarified sufficiently. For example, there may be no one who d...
Discute-se a base de construção do conceito de risco, a partir da descrição do modelo de inferência ...
Causal inference based on a restricted version of the potential outcomes approach reasoning is assum...
One of the more challenging issues in epidemiological research is being able to provide an unbiased ...
This chapter explores the idea that causal inference is warranted if and only if the mechanism under...
This book compiles and presents new developments in statistical causal inference. The accompanying d...
This chapter explores the idea that causal inference is warranted if and only if the mechanism under...
Epidemiologists typically seek to answer causal questions using statistical data:we observe a statis...
Inferring causality is necessary to achieve the goal of epidemiology, which is to elucidate the caus...
Increasingly, modern epidemiology has adopted complex causal frameworks incorporating individual- an...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Re...
Social factors are associated with a wide variety of health outcomes. Social epidemiology has succes...
There has been much debate about the relative emphasis of the field of epidemiology on causal infere...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
The causes of a disease have not been clarified sufficiently. For example, there may be no one who d...
Discute-se a base de construção do conceito de risco, a partir da descrição do modelo de inferência ...
Causal inference based on a restricted version of the potential outcomes approach reasoning is assum...