Probabilistic Causality in Longitudinal StudiesIn 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. |
Contents
PREFACE | 1 |
PREDICTIVE CAUSAL INFERENCE IN A SERIES OF EVENTS | 29 |
CONFIDENCE STATEMENTS ABOUT THE PREDICTION PROCESS | 44 |
Copyright | |
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Aalen acute GvHD Arjas assumed asymptotic variance C₁ causal chain causal dependence causal effects causal events cause censoring Chapter child psychiatric incidence chronic GVHD CMV-infection conditional independence confidence bands confidence limits confounding consider corresponds counterfactual cumulative hazards curves death in remission defined DEPENDENT COVARIATES Diff distribution dynamic Eerola example exposure FIXED COVARIATES function g(Ts given hazard models hypothetical inference innovation gains interpretation Kaplan-Meier estimator log(t logistic regression marked point process martingale Norros observed occurred previously outcome P(Tc PARAMETER patient point process possible prediction interval prediction method prediction probabilities prediction process prediction t=Tc Prob probabilistic causality propensities psychiatric admission random variables regression coefficients relapse and death relapse-free survival response risk difference risk factors risk function Rubin Rubin's model Springer-Verlag statistical models stochastic processes studies subjective probability Suppes survival analysis t=TG t₁ theory time-dependent transplantation u\TA values