%0 Journal Article
%T Causal mediation analysis: How to avoid fooling yourself that X causes Y.
%A Lazic SE
%J Lab Anim
%V 0
%N 0
%D 2024 Aug 11
%M 39129196
%F 2.908
%R 10.1177/00236772231217777
%X The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.