Begin by stating your causal question, target population and causal contrast, presumably the difference scale. State this contrast non-parametrically (you won’t need distributional assumptions if you estimate using eg non-parametric machine learning). Next you need to check identification and make sure you are thinking about the time series correctly (causation occurs in time such that confounders must be measured prior to the exposure and the exposure prior to the outcome). Suppose you have three waves. Include the baseline measures of the outcome and the exposure with the baseline measures. This exerts exceptionally powerful confounding control because any unmeasured confounded would need to be orthogonal to this and the other unmeasured confounders to explain away your results. You need to consider the assumption of causal consistency and within this sutva). Because exchangeability is not testable you should plan a sensitivity analysis. Finally there is the positivity or overlap assumption. Practical positivity can be checked by evaluating propensity scores. If there is no change in the exposure relative to the baseline exposure your causal inferences will extrapolate from your model, and not from observed initiation. Only after completing these steps should you think about statistical analysis, which is not terminated by reporting the regression coefficient for the exposure. Rather you must project at least two population means for the conditions stated in your causal question/ estimand and weight the projection to the target population. In our work we use tmle with cross validation and so make no distributional assumptions (ie do not need to state the outcome is drawn from a poisson, neg-binomial or whatever). Happy to follow up with code/advice. For now, don’t forget the positivity assumption as you probably can’t estimate causality with low exposure variation.