Causal inference with compositional data

Abstract

Compositional data is a form of hierarchical data in which a whole (or a total) is the sum of its constituent components. Although compositional data can arise in any setting, they
are particularly common in health research, with typical examples including dietary, physical activity, or microbiome data. This type of data can bring a range of analytical and interpretational challenges which risks
results being misinterpreted.

In this talk, I will introduce compositional data using directed acrylic graphs (DAGs), outline the different types of causal effects that may be of interest in such data, and discuss suitable analytical approaches.