My question relates to causal inference in longitudinal data setting.
I have a dataset which includes certain measurements from same individuals at two different time points, say at baseline and at 12 months. My main research question is whether a change in a structural variable A causes a negative change in a physiologial outcome Y (“Does A cause Y?”). A is an ordinal variable and Y is continuous. Ordinal variable B is another structural variable and it has been previously shown that that B causes Y meaning that worsening status in B causes a negative change in Y. I have a reason to believe that A is weakly correlated with B.
If I aim to study whether A causes Y, what sort of a model I should build? Generally I should not regress a change score so I would take Y at 12 months as an outcome and Y at baseline as a covariate. Or would it be appropriate in this case to regress change in Y? And how should I deal with A? Is it appropriate to take a change value from A and add that as a covariate? Or should I have somekind kind of an interaction term? Should I include B in a similar way because it seems to be a confounder (causes both A and Y) based on prior literature?
Fortuitously, I came across this very nicely looking paper which was recently published but as far I understand it discusses only cross-sectional data: https://academic.oup.com/ije/article/51/5/1604/6294759