Causal Analysis of Compositional Data
A causal view on compositional data
Many scientific datasets are compositional in nature.Here, we provide a causal view on compositional data in an instrumental variable setting where the composition acts as the cause.Focusing on modern high-dimensional microbiomesequencing data as a timely illustrative use case, our analysis first reveals that popular one-dimensional information-theoretic summary statistics, such as diversity and richness, may be insufficient for drawing causal conclusions from ecological data.Instead, we advocate for multivariate alternatives using statistical data transformations and regression techniques that take the special structure of the compositional sample space into account.In a comparative analysis on synthetic and semi-synthetic data we show the advantages and limitations of our proposal and posit that our framework mayprovide a useful starting point for cause-effect estimation in the context of compositional data.
