by Manipulability — Pearl vs Rubin (wonkish) Pearl asserts, while some RCM (Rubin Causal Models) theorists deny, that so-called “non-manipulable” variables can be causes (Pearl 2019; Holland 1986, 2008). Race and gender, which arguably cannot be experimentally manipulated, are key examples of such variables … My response is that although advocates of the frameworks adopt conflicting positions regarding certain variables, these positions are not forced upon them by their frameworks. When one moves away from thorny variables such as race and gender and looks at debates regarding slightly less contentious variables such as obesity … Whereas RCM modelers link potential outcomes to particular experimental manipulations, SCM (Structural Causal Models) modelers
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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Manipulability — Pearl vs Rubin (wonkish)
Pearl asserts, while some RCM (Rubin Causal Models) theorists deny, that so-called “non-manipulable” variables can be causes (Pearl 2019; Holland 1986, 2008). Race and gender, which arguably cannot be experimentally manipulated, are key examples of such variables …
My response is that although advocates of the frameworks adopt conflicting positions regarding certain variables, these positions are not forced upon them by their frameworks. When one moves away from thorny variables such as race and gender and looks at debates regarding slightly less contentious variables such as obesity … Whereas RCM modelers link potential outcomes to particular experimental manipulations, SCM (Structural Causal Models) modelers represent manipulations by formally applying the do-operator to variables in a graph …
Admittedly, Pearl does assert that that one can intervene upon gender without specifying a manipulation. He would, however, require “do(gender)” to be well-defined, which requires there be at least hypothetical manipulations on gender (perhaps available only to “Lady Nature herself” (Pearl 2018, p. 4)). Whether such a manipulation is coherent is debatable, and resolving this debate would require careful attention to the purportedly non-manipulable variable. Given SCM modelers’ willingness to characterize interventions in a way that abstracts away from concrete manipulations, it is unsurprising that they would have a higher tolerance than RCM modelers for talk of hypothetical manipulations. Yet the frameworks themselves do not settle what one should say about particular “non-manipulable” variables.