Is one of the methods of getting causal estimates that you're going to write about simply to give each possible decision a non-zero random chance of being selected? Here's what Hanson wrote about this approach:
Conditioning on random decisions should very effective, but seems expensive. However, it seems much less expensive to sometimes randomly not do a policy change that you were going to do, than to sometimes randomly do a policy change you were not going to do. So I suggest that a futarchy system for considering and adopting proposals randomly reject say 5% of the changes that it would otherwise have accepted. This should ensure good estimates conditional on not adopting proposals, leaving only the potential problem of a decision selection bias distorting estimates conditional on adopting some proposal.
Is one of the methods of getting causal estimates that you're going to write about simply to give each possible decision a non-zero random chance of being selected? Here's what Hanson wrote about this approach:
https://open.substack.com/pub/overcomingbias/p/decision-selection-bias