In this presentation, I will address key aspects of the statistical modelling of events in (continuous) time. This is for instance relevant when some events correspond to some "treatment" and others to important outcomes such as "relapse" or "death".
First we discuss the visual representation of multivariate dependence structures among events based on marked point processes, using the concept of local independence and associated graphs (Didelez, 2008). It will be shown how reasoning and inference for incompletely observed systems can be facilitated using such graphical representation and a suitable notion of graph-separation (Didelez, 2006). Secondly we present a formal notion of causal relations between events (or processes) in time based on a decision theoretic approach (Dawid and Didelez, 2010; Didelez, 2015), and discuss the use of local independence graphs to decide the question of identifiability in incompletely observed systems. This will be illustrated with an application to cancer screening in Norway (Roysland, Didelez et al., 2018) as well as with inference under drop out (Farewell, Huang & Didelez, 2017).
The presentation will focus on basic principles and concepts rather than technical details.

References:
- Dawid and Didelez (2010). Identifying the consequences of dynamic treatment strategies: A decision theoretic overview, Statistics Surveys, 4, 184-231.
- Didelez (2006). Asymmetric Separation for Local Independence Graphs, in: Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence, 130-137.
- Didelez (2008). Graphical models for marked point processes based on local independence. JRSS(B), 70, 245-264.
- Didelez (2015). Causal Reasoning for events in continuous time: a decision–theoretic approach. Proceedings of the 31st Annual Conference on Uncertainty in Artificial Intelligence - Causality Workshop (Invited Paper).
- Farewell, Huang, Didelez (2017). Ignorability for general longitudinal data. Biometrika, 104(2), 317-326.
- Røysland, Didelez, Nygard, Lange, Aalen (2018). Causal reasoning in survival analysis:  reweighting and local independence graphs. In preparation.

Einladung von Prof. Jens Rademacher