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Graphs, Time and Causal Inference



Datum: 09.01.2018
Uhrzeit: 16:00 Uhr

[(im neu verlängerten Flur an Raum 6240 vorbei)]
Ort: MZH 6210


Vortragende(r): Prof. Vanessa Didelez Homepage - Vortragende(r) (Leibniz Institute for Prevention Research and Epidemiology - BIPS und Fachbereich Mathematik und Informatik Universität Bremen )

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.



Ansprechpartner(in) / Einladende(r):
Prof . Jens Rademacher





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