Uncovering life-course patterns with causal discovery and survival analysis

TL;DR

This paper introduces a novel bi-level approach combining causal discovery and survival analysis to model life event choices and timings, providing a data-driven understanding of life-course dynamics.

Contribution

It is the first to formulate a bi-level model integrating causal discovery with survival analysis for life-course event analysis.

Findings

Causal relationships between life events were identified from survey data.

Socio-demographic factors influence transition probabilities between life events.

The approach offers a data-driven alternative to expert-based causal assumptions.

Abstract

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.