  The goal of this paper is to design a causal inference method accounting for
complex interactions between causal factors. The proposed method relies on a
category theoretical reformulation of the definitions of dependent variables,
independent variables and latent variables in terms of products and arrows in
the category of unlabeled partitions. Throughout the paper, we demonstrate how
the proposed method accounts for possible hidden variables, such as
environmental variables or noise, and how it can be interpreted statistically
in terms of $p$-values. This interpretation, from category theory to
statistics, is implemented through a collection of propositions highlighting
the functorial properties of ANOVA. We use these properties in combination with
our category theoretical framework to provide solutions to causal inference
problems with both sound algebraic and statistical properties. As an
application, we show how the proposed method can be used to design a
combinatorial genome-wide association algorithm for the field of genetics.
