Should be doing stuff like this, if you want to understand effects of masks:https://arxiv.org/pdf/2103.04472.pdf
https://auai.org/uai2021/pdf/uai2021.89.preliminary.pdf (this really is preliminary, e.g. they have not yet uploaded a newer version that incorporates peer review suggestions).---Can't do stuff in the second paper without worrying about stuff in the first (unless your model is very simple).
Pretty interesting.Since you are interested in policies that operate along some paths only, you might find these of interest:https://pubmed.ncbi.nlm.nih.gov/31565035/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330047/We have some recent stuff on generalizing MDPs to have a causal model inside every state ('path dependent structural equation models', to appear in UAI this year).
3: No, that will never work with DL by itself (e.g. as fancy regressions).4: No, that will never work with DL by itself (e.g. as fancy regressions).5: I don't understand this question, but people already use DL for RL, so the "support" part is already true. If the question is asking whether DL can substitute for doing interventions, then the answer is a very qualified "yes," but the secret sauce isn't DL, it's other things (e.g. causal inference) that use DL as a subroutine.---The problem is, most folks who aren't doing data science for a living themselves only view data science advances through the vein of hype, fashion trends, and press releases, and so get an entirely wrong sense of what is truly groundbreaking and important.
If there is, I don’t know it.
There's a ton of work on general sensitivity analysis in the semi-parametric stats literature.
If there is really both reverse causation and regular causation between Xr and Y, you have a cycle, and you have to explain what the semantics of that cycle are (not a deal breaker, but not so simple to do. For example if you think the cycle really represents mutual causation over time, what you really should do is unroll your causal diagram so it's a DAG over time, and redo the problem there).You might be interested in this paper (https://arxiv.org/pdf/1611.09414.pdf) that splits the outcome rather than the treatment (although I don't really endorse that paper).
The real question is, why should Xc be unconfounded with Y? In an RCT you get lack of confounding by study design (but then we don't need to split the treatment at all). But this is not really realistic in general -- can you think of some practical examples where you would get lucky in this way?
"That’s the test. Would you put it in your arm rather than do nothing? And if the answer here is no, then, please, show your work."
Seems to be an odd position to take to shift the burden of proof onto the vaccine taker rather than than the scientist.
---I think a lot of people, you included, are way overconfident on how transmissible B.1.1.7. is.