Description
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.
Springer Book Archives
Book Information
ISBN 9780387943671
Author Mervi Eerola
Format Paperback
Page Count 131
Imprint Springer-Verlag New York Inc.
Publisher Springer-Verlag New York Inc.
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.
Springer Book Archives
Book Information
ISBN 9780387943671
Author Mervi Eerola
Format Paperback
Page Count 131
Imprint Springer-Verlag New York Inc.
Publisher Springer-Verlag New York Inc.
