Causality, Mediation and Time: A Dynamic Viewpoint

Abstract

Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented.

Understanding mechanisms: opening the “black box” in observational studies

“…The aim is to analyze observational data in a way that mimics randomized experiments. Among the options for making counterfactual deductions from observational data, marginal structural model (MSM) with inverse probability weighting (IPW) and the use of causal diagrams (directed acyclic graphs or DAG) is the most common (16,20,22). This approach is well suited for causal mediation analysis (16).…”

“…This approach is well suited for causal mediation analysis (16). Dynamic path analysis is an alternative for estimating direct and indirect effects (17,20,23). This method is particularly useful when time-to-event is the outcome, ie, in survival analysis (17,20).…”

“…Analysis of time-to-event data in Cox regression and comparing models with and without the mediator involves additional problems because the important assumption of proportional hazards can never be satisfied for both models. One alternative to the traditional regression methods (eg, logistic or Cox regression) is to apply a counterfactual framework (20). The concept behind this is, to a large degree, based on ideas of intervention described in Judea Pearl's famous book "Causality" (21).…”

“…Counterfactual frameworks could be a valuable basis for analytical progress and increased causal understanding of social and biologic processes and, thereby, for correct interventions. These causal methods have limitations [eg, dependent on the assumption of no unmeasured confounding of exposure or mediator (19,20)]. This problem should be minimized by the careful collection of covariates that could act as confounders of the exposure-or mediator-outcome relationships (19) as well as sensitivity analysis assessments (25).…”

“…Dynamic path analysis is an alternative for estimating direct and indirect effects (17,20,23). This method is particularly useful when time-to-event is the outcome, ie, in survival analysis (17,20). Lange & Hansen (17) estimated the mediating role of the physical work environment on the relationship between SES and sickness absence.…”