{Reference Type}: Journal Article
{Title}: Causal mediation analysis: How to avoid fooling yourself that X causes Y.
{Author}: Lazic SE;
{Journal}: Lab Anim
{Volume}: 0
{Issue}: 0
{Year}: 2024 Aug 11
{Factor}: 2.908
{DOI}: 10.1177/00236772231217777
{Abstract}: 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.