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.
