I’d like to share a recently published paper written by Hernan.
Causal analyses of existing databases: no power calculations required (https://www.jclinepi.com/article/S0895-4356(21)00273-0/fulltext)
Hernan claims that: "Causal effects are not binary signals that are either detected or undetected; causal effects are numerical quantities that need to be estimated. Because the goal is to quantify the effect as unbiasedly and precisely as possible, the solution to observational analyses with imprecise effect estimates is not avoiding observational analyses with imprecise estimates, but rather encouraging the conduct of many observational analyses. It is preferable to have multiple studies with imprecise estimates than having no study at all
If a causal question is important, analyze your data, publish your estimates, encourage others to do the same, and then meta-analyze. The alternative is an unanswered question.
Dr. Yanover from KI institute told me about this paper. Thanks!