  We provide a novel approach and an exploratory study for modelling life event
choices and occurrence from a probabilistic perspective through causal
discovery and survival analysis. Our approach is formulated as a bi-level
problem. In the upper level, we build the life events graph, using causal
discovery tools. In the lower level, for the pairs of life events,
time-to-event modelling through survival analysis is applied to model
time-dependent transition probabilities. Several life events were analysed,
such as getting married, buying a new car, child birth, home relocation and
divorce, together with the socio-demographic attributes for survival modelling,
some of which are age, nationality, number of children, number of cars and home
ownership. The data originates from a survey conducted in Dortmund, Germany,
with the questionnaire containing a series of retrospective questions about
residential and employment biography, travel behaviour and holiday trips, as
well as socio-economic characteristic. Although survival analysis has been used
in the past to analyse life-course data, this is the first time that a bi-level
model has been formulated. The inclusion of a causal discovery algorithm in the
upper-level allows us to first identify causal relationships between
life-course events and then understand the factors that might influence
transition rates between events. This is very different from more classic
choice models where causal relationships are subject to expert interpretations
based on model results.
