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Probabilistic Causality in Longitudinal Studies

  • MerviĀ Eerola

Part of the Lecture Notes in Statistics book series (LNS, volume 92)

Table of contents

  1. Front Matter
    Pages N1-viii
  2. Mervi Eerola
    Pages 1-28
  3. Mervi Eerola
    Pages 57-108
  4. Mervi Eerola
    Pages 109-111
  5. Back Matter
    Pages 112-137

About this book

Introduction

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.

Keywords

Censoring Logistic Regression Longitudinal studies Randomized experiment innovation point process statistics

Authors and affiliations

  • MerviĀ Eerola
    • 1
  1. 1.Department of StatisticsUniversity of HelsinkiHelsinkiFinland

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-2684-0
  • Copyright Information Springer-Verlag New York 1994
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94367-1
  • Online ISBN 978-1-4612-2684-0
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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