B1149
Title: Graphical criteria for the identification of causal effects in event-history analyses
Authors: Kjetil Roysland - Univeristy of Oslo (Norway) [presenting]
Abstract: Continuous-time survival or more general event-history settings are considered, where the aim is to infer the causal effect of a time-dependent treatment process. This is formalised as the effect on the outcome event of a (possibly hypothetical) intervention on the intensity of the treatment process, i.e. a stochastic intervention. To establish whether valid inference about the interventional situation can be drawn from typical observational, i.e. non-experimental data, graphical rules are proposed, indicating whether the observed information is sufficient to identify the desired causal effect by suitable re-weighting. In analogy to the well-known causal-directed acyclic graphs, the corresponding dynamic graphs combine causal semantics with local independence models for multivariate counting processes. Importantly, causal inference from censored data requires structural assumptions on the censoring process beyond the usual independent censoring assumption, which can be represented and verified graphically. The results establish general non-parametric identifiability and do not rely on particular survival models.
