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By all means. I hereby advocate, as always, that data-science rests on two equally important pillars: causal inference and statistical estimation. However, a glaring asymmetry can be seen today in academia: researchers in the CI pillars are thirsty for new
2/3 tools from the estimation pillar but not the other way. By and large, leaders of the stat pillars have zero interest in advances emerging from the CI pillar. The great majority of them truly believe in "do it the classical way" and "a causal model is a special case of a
3/3 predictive model". Moreover, in academia, the stat pillar dominates its CI partner by 100:1 ratio, and now insists on total dominion in the name of "its just a special case". Thus, to achieve equilibrium, I think CI needs academic autonomy, at least for a while. #Bookofwhy
