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Where is a conditional average treatment effect insufficient? We want to say that, seeing X right now, if we do a1, we'll see a change of d (+/- D), compared to
by 6gvONxR4sf7o 6y ago
Where is a conditional average treatment effect insufficient? We want to say that, seeing X right now, if we do a1, we'll see a change of d (+/- D), compared to doing a0. Or being able to predict y1 and y0 corresponding to actions a1 and a0. That would be a huge step, and something some of ML's most useful methods can't do.
- zwaps 6y agoDAGs are not the only valid approach to causality, nor do they subsume other approaches (I have previously commented on this during the Gelman&Pearl debates). As you suggest, the treatment effect and econometrics literature currently is on a semi-parametric trend: Given that they don't believe that one can actually produce a believable complete causal model (or DAG), one tries to estimate a treatment effect that does not depend on parametric or functional assumptions.
- a9h74j 6y ago> DAGs are not the only valid approach to causality It has been a truism to some that "most efforts in policy are responses to previous efforts in policy." In SPC you can measure noise and overcontrol. With a long-lived policy-making institution [unusual?] honesty might admit to facing effects of previous well-intentioned policies.