What is an alternative way to optimize for ads budget allocation?

As far as I know, RCT (A/B testing) is the golden standard to test for different factor effect. But that assume the distribution don’t drift overtime, and is cost intensive

Another method I found is Marketing Mix Modeling. But this seems to riddle with biases and pitfall, which stemmed from heavy induction bias, and treating correlation as casual model

As my understanding goes, there is a way to reduce confounding effect through causal inference

However, casual inference requires causal graph. This can be done with causal graph discovery, but it impose a lot of (unverifiable) assumptions on the data generation process. So a domain specific causal graph construction is still needed (which is not ideal)

So are there alternative methods to ROI? Also is there a stochastic process that model this problem? Pinning link of related topic/research is also welcome