by Causal assumptions in need of careful justification As is brilliantly attested by the work of Pearl, an extensive and fruitful theory of causality can be erected upon the foundation of a Pearlian DAG. So, when we can assume that a certain DAG is indeed a Pearlian DAG representation of a system, we can apply that theory to further our causal understanding of the system. But this leaves entirely untouched the vital questions: when is a Pearlian DAG representation of a system appropriate at all?; and, when it is, when can a specific DAG D be regarded as filling this rôle? As we have seen, Pearlian representability requires many strong relationships to hold between the behaviours of the system under various kinds of interventions. Causal discovery
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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Causal assumptions in need of careful justification
As is brilliantly attested by the work of Pearl, an extensive and fruitful theory of causality can be erected upon the foundation of a Pearlian DAG. So, when we can assume that a certain DAG is indeed a Pearlian DAG representation of a system, we can apply that theory to further our causal understanding of the system. But this leaves entirely untouched the vital questions: when is a Pearlian DAG representation of a system appropriate at all?; and, when it is, when can a specific DAG D be regarded as filling this rôle? As we have seen, Pearlian representability requires many strong relationships to hold between the behaviours of the system under various kinds of interventions.
Causal discovery algorithms … similarly rely on strong assumptions … The need for such assumptions chimes with Cartwright’s maxim “No causes in, no causes out”, and goes to refute the apparently widespread belief that we are in possession of a soundly-based technology for drawing causal conclusions from purely observational data, without further assumptions …
In my view, the strong assumptions needed even to get started with causal interpretation of a DAG are far from self-evident as a matter of course, and whenever such an interpretation is proposed in a real-world context these assumptions should be carefully considered and justified. Without such justification, why should we have any faith at all in, say, the application of Pearl’s causal theory, or in the output of causal discovery algorithms?
But what would count as justification? … It cannot be conducted entirely within a model, but must, as a matter of logic, involve consideration of the interpretation of the terms in the model in the real world.