  Background: In longitudinal data, it is common to create 'change scores' by
subtracting measurements taken at baseline from those taken at follow-up, and
then to analyse the resulting 'change' as the outcome variable. In
observational data, this approach can produce misleading causal effect
estimates. The present article uses directed acyclic graphs (DAGs) and simple
simulations to provide an accessible explanation of why change scores do not
estimate causal effects in observational data.
  Methods: Data were simulated to match three general scenarios where the
variable representing measurements of the outcome at baseline was a 1)
competing exposure, 2) confounder, or 3) mediator for the total causal effect
of the exposure on the variable representing measurements of the outcome at
follow-up. Regression coefficients were compared between change-score analyses
and DAG-informed analyses.
  Results: Change-score analyses do not provide meaningful causal effect
estimates unless the variable representing measurements of the outcome at
baseline is a competing exposure, as in a randomised experiment. Where such
variables (i.e. baseline measurements of the outcome) are confounders or
mediators, the conclusions drawn from analyses of change scores diverge
(potentially substantially) from those of DAG-informed analyses.
  Conclusions: Future observational studies that seek causal effect estimates
should avoid analysing change scores and adopt alternative analytical
strategies.
