Recent developments in causal mediation analysis

Rhian Daniel, London School of Hygiene and Tropical Medicine. Part of the statistics seminars series.

In diverse fields of empirical research, including many in the biological sciences, attempts are made to decompose the effect of an exposure on an outcome into its effects via different pathways. For example, it is well-established that breast cancer survival rates in the UK differ by socio-economic status. But how much of this effect is due to differential adherence to screening programmes? How much is explained by treatment choices? And so on. These enquiries, traditionally tackled using simple regression methods, have been given much recent attention in the causal inference literature, specifically in the fruitful area known as Casual Mediation Analysis. The focus has mainly been on so-called natural direct and indirect effects, with flexible estimation methods that allow their estimation in the presence of non-linearities and interactions, and careful consideration given to the need for controlling confounding. Despite these many developments, the estimation of natural direct and indirect effects is still plagued by one major limitation, namely its reliance on an assumption known as the "cross-world" assumption, an assumption so strong that no experiment could even hypothetically be designed under which its validity would be guaranteed. Moreover, the assumption is known to be violated when confounders of the mediator-outcome association are affected by the exposure, and thus in particular in settings that involve repeatedly measured mediators, or multiple correlated mediators. In this talk, I will discuss alternative mediation effects known as interventional direct and indirect effects, (VanderWeele et al, Epidemiology, 2014), and a novel extension to the multiple mediator setting.

This is joint work with Stijn Vansteelandt, University of Gent. We argue that interventional direct and indirect effects are policy-relevant and show that they can be identified under much weaker conditions than natural direct and indirect effects. In particular, they can be used to capture the path-specific effects of an exposure on an outcome that are mediated by distinct mediators, even when, as often, the structural dependence between the multiple mediators is unknown. The approach will be illustrated using data on breast cancer survival. Finally, I will discuss extensions of this approach to settings with high-dimensional mediators.

Rhian Daniel, London School of Hygiene and Tropical Medicine