Ebook: Probabilistic Causality in Longitudinal Studies

In many applied fields of statistics the concept of causality is central to a scientific investigation. The author's aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time.
The proposed prediction method in this study provides a framework to study the dynamics and the magnitudes of causal effects in a series of dependent events. Its usefulness is demonstrated by the analysis of two examples both drawn from biomedicine, one on bone marrow transplants and one on mental hospitalization.
Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.

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In many applied fields of statistics the concept of causality is central to a scientific investigation. The author's aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time.
The proposed prediction method in this study provides a framework to study the dynamics and the magnitudes of causal effects in a series of dependent events. Its usefulness is demonstrated by the analysis of two examples both drawn from biomedicine, one on bone marrow transplants and one on mental hospitalization.
Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.

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In many applied fields of statistics the concept of causality is central to a scientific investigation. The author's aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time.
The proposed prediction method in this study provides a framework to study the dynamics and the magnitudes of causal effects in a series of dependent events. Its usefulness is demonstrated by the analysis of two examples both drawn from biomedicine, one on bone marrow transplants and one on mental hospitalization.
Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.
Content:
Front Matter....Pages N1-viii
Foundations of Probabilistic Causality....Pages 1-28
Predictive Causal Inference in A Series Of Events....Pages 29-43
Confidence Statements about the Prediction Process....Pages 44-56
Applications....Pages 57-108
Concluding Remarks....Pages 109-111
Back Matter....Pages 112-137

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In many applied fields of statistics the concept of causality is central to a scientific investigation. The author's aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time.
The proposed prediction method in this study provides a framework to study the dynamics and the magnitudes of causal effects in a series of dependent events. Its usefulness is demonstrated by the analysis of two examples both drawn from biomedicine, one on bone marrow transplants and one on mental hospitalization.
Consequently, statistical researchers and other scientists concerned with identifying causal relationships will find this an interesting and new approach to this problem.
Content:
Front Matter....Pages N1-viii
Foundations of Probabilistic Causality....Pages 1-28
Predictive Causal Inference in A Series Of Events....Pages 29-43
Confidence Statements about the Prediction Process....Pages 44-56
Applications....Pages 57-108
Concluding Remarks....Pages 109-111
Back Matter....Pages 112-137
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