I think there’s something to be said that if the qualitative stuff looks bad (e.g. experts are negative, studies are bunch of non-randomized stuff susceptible to endogeneity concerns), you always have the option of just implementing aggressive discounts to the CEA, and the fact that it looks too good means the CEA is done poorly, not that the CEA-focused approach is unreliable.
Makes sense. I also like explicit quantification because it is more transparent, making it easier to examine assumptions, identify main uncertainties, and therefore improve estimates in the future. With approaches associated with cluster thinking, I think it is often unclear which assumptions are driving the decisions, or whether the decisions being made actually follow from the assumptions.
I think there’s something to be said that if the qualitative stuff looks bad (e.g. experts are negative, studies are bunch of non-randomized stuff susceptible to endogeneity concerns), you always have the option of just implementing aggressive discounts to the CEA, and the fact that it looks too good means the CEA is done poorly, not that the CEA-focused approach is unreliable.
Makes sense. I also like explicit quantification because it is more transparent, making it easier to examine assumptions, identify main uncertainties, and therefore improve estimates in the future. With approaches associated with cluster thinking, I think it is often unclear which assumptions are driving the decisions, or whether the decisions being made actually follow from the assumptions.