Thank you so much for writing this! Yes, this is mostly an accurate summary of my views (although I would certainly phrase some things differently). I just want to point out two minor disagreements:
I don’t think the problem is that doctors are too rushed to do a proper job, I think the patient-specific data that you would need is in many cases theoretically unobservable, or at least that we would need a much more complete understanding of biological mechanisms in order to know what to test the patients for in order to make a truly individualized decision. At least for the foreseeable future, I think it will be impossible for doctors to determine which patients will benefit on an individual level, they will be constrained to using the patient’s observables to put them in a reference group, and then use that reference group to predict risk based on observations from other patients in the same reference group
I am not entirely convinced that the Pearlian approach is the most natural way to handle this. In the manuscript, I use “modern causal models” as a more general term that also includes other types of counterfactual causal models. Of course, all these models are basically isomorphic, and Cinelli/Pearl did show in response to my last paper that it is possible to do the same thing using DAGs. I am just not at all convinced that the easiest way to capture the relevant intuition is to use the Pearl’s graphical representation of the causal models.
Thank you so much for writing this! Yes, this is mostly an accurate summary of my views (although I would certainly phrase some things differently). I just want to point out two minor disagreements:
I don’t think the problem is that doctors are too rushed to do a proper job, I think the patient-specific data that you would need is in many cases theoretically unobservable, or at least that we would need a much more complete understanding of biological mechanisms in order to know what to test the patients for in order to make a truly individualized decision. At least for the foreseeable future, I think it will be impossible for doctors to determine which patients will benefit on an individual level, they will be constrained to using the patient’s observables to put them in a reference group, and then use that reference group to predict risk based on observations from other patients in the same reference group
I am not entirely convinced that the Pearlian approach is the most natural way to handle this. In the manuscript, I use “modern causal models” as a more general term that also includes other types of counterfactual causal models. Of course, all these models are basically isomorphic, and Cinelli/Pearl did show in response to my last paper that it is possible to do the same thing using DAGs. I am just not at all convinced that the easiest way to capture the relevant intuition is to use the Pearl’s graphical representation of the causal models.