The stochastic system approach to causality with a view toward lifecourse epidemiology

TL;DR

This paper revisits a causality approach based on physical laws and systems, emphasizing its suitability for lifecourse epidemiology through a stochastic system framework and a conceptual model for coronary heart disease.

Contribution

It introduces the stochastic system approach to causality, tailored for lifecourse epidemiology, integrating multivariate stochastic processes and mixed state-space modeling.

Findings

Proposes a hierarchy of factors modeled by multivariate stochastic processes.

Develops a conceptual model for coronary heart disease using mixed state-space processes.

Highlights the relevance of levels and effects in a causality framework.

Abstract

The approach of causality based on physical laws and systems is revisited. The issue of "levels", the relevance to epidemiology and the definition of effects are particularly developed. Moreover it is argued that this approach that we call the stochastic system approach is particularly well fitted to study lifecourse epidemiology. A hierarchy of factors is described that could be modeled using a suitable multivariate stochastic process. To illustrate this approach, a conceptual model for coronary heart disease mixing continuous and discrete state-space processes is proposed.