Abstract

In this presentation, I will reflect on why a formal causal treatment of mediation is conceptually and technically demanding, even though it feels so natural to speak of direct and indirect effects. Standard causal mediation analysis relies on nested counterfactuals and a cross-world independence assumption to define and identify natural (in)direct effects; their identification in longitudinal or survival settings is essentially hopeless. An alternative interventionist view (Robins & Richardson, 2011), closely aligned in spirit with the decision-theoretic approach of Dawid (2021), leads in the longitudinal/time-to-event case to separable treatment effects (Didelez, 2019; Stensrud et al, 2022). I will examine whether and how this approach addresses the challenges. Finally, when assumptions fail, partial identification through bounds can be considered; for separable effects, these bounds are closely related to existing bounds for specific natural and path-specific effects (Breum et al, 2025). References:Breum, Didelez, Gabriel, Sachs, (2025). Bounds for causal mediation effects. arXiv preprint arXiv:2512.11549.Dawid (2021). Decision-theoretic foundations for statistical causality, Journal of Causal Inference, 9(1), 39-77. https://doi.org/10.1515/jci-2020-0008Didelez (2019). Defining causal mediation with a longitudinal mediator and a survival outcome, Lifetime Data Analysis 25, 593-610 Robins JM, Richardson TS (2011). Alternative graphical causal models and the identification of direct effects. In: Causality and psychopathology: finding the determinants of disorders and their cures. Oxford University PressStensrud, Young, Didelez, Robins, Hernán (2022). Separable effects for causal inference in the presence of competing events, JASA 117 (537), 175-183