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1/ I am recommending this paper to every data analyst educated by traditional textbooks, which start with
regression equations, add and delete regressors, estimate and compare coefficients before and after
deletion, and then ask which coefficient has "causal interpretation"
regression equations, add and delete regressors, estimate and compare coefficients before and after
deletion, and then ask which coefficient has "causal interpretation"
2/ I was shocked to realize that the majority of data analysts today are products of this culture, trapped in endless confusion, with little chance to snap out of it, since journal editors, reviewers and hiring committees
are trapped in the same culture. The new PO framework does
are trapped in the same culture. The new PO framework does
3/ offer a theoretical escape route from this culture, through the assumption of "conditional ignorability" but, since it is congnitively formidable, practicing analysts must rely on regression arguments.
Keele et al examine a causal model (Fig.2) and ask: suppose we regress Y
Keele et al examine a causal model (Fig.2) and ask: suppose we regress Y
4/ on all observed variables; which of the coefficients has any causal interpretation. I have alerted economists to such questions here ucla.in/2mhxKdO
(3.2.7) so, we can assume they have mastered the techniques by now. Students of #Bookofwhy seeking a gentle way to
(3.2.7) so, we can assume they have mastered the techniques by now. Students of #Bookofwhy seeking a gentle way to
5/5 approach their mentors or professors or their peers, this is a great channel to motivate them.
