Overview

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.

Full Product Details

Author:   Mervi Eerola
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1994
Volume:   92
Dimensions:   Width: 15.50cm , Height: 0.80cm , Length: 23.50cm
Weight:   0.260kg
ISBN:  

9780387943671

ISBN 10:   0387943676
Pages:   131
Publication Date:   07 October 1994
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

1. Foundations of Probabilistic Causality.- 1.1 Introduction.- 1.2 Historical aspects of causality.- 1.3 Probabilistic causality.- 1.3.1 I.J. Good: A quantitative theory of probabilistic causality.- 1.3.2 P. Suppes: A qualitative theory of probabilistic causality.- 1.4 Different interpretations of probability in causality.- 1.4.1 Physical probabilities.- 1.4.2 Epistemic probabilities.- 1.5 Counterfactuals in causality.- 1.6 Causality in statistical analysis.- 1.6.1 Randomized experiments.- 1.6.2 Independence models.- 1.6.3 Dynamic models.- 1.7 Discussion.- 2. Predictive Causal Inference in a Series of Events.- 2.1 Introduction.- 2.2 The mathematical framework: marked point processes.- 2.3 The prediction process associated with a marked point process.- 2.4 A hypothetical example of cumulating causes.- 2.5 Causal transmission in terms of the prediction process.- 3. Confidence Statements About the Prediction Process.- 3.1 Introduction.- 3.2 Prediction probabilities in the logistic regression model.- 3.3 Confidence limits for ?t using the delta-method.- 3.4 Confidence limits for ?t fit based on the monotonicity of hazards.- 3.4.1 Confidence limits for the hazard in the logistic model.- 3.4.2 Stochastic order of failure time vectors.- 3.5 Discussion.- 4. Applications.- 4.1 Multistate models in follow-up studies.- 4.2 Modelling dependence between causal events.- 4.3 Two applications.- 4.3.1 The Nordic bone marrow transplantation data: the effects of CMV infection and chronic GvHD on leukemia relapse and death.- 4.3.2 The 1955 Helsinki cohort: the effect of childhood separation on subsequent mental hospitalisations.- 4.4 Sensitivity of the innovation gains on hazard specification.- 4.5 Discussion.- 4.6 Computations.- 4.7 Further uses of the method.- 4.7.1 Quantitative causal factors.- 4.7.2 Informative censoring and drop-out.- 5. Concluding Remarks.- Appendices 1-2.

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