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Causal Inference Sequence Part 1: Basic Terminology and the Assumptions of Causal Inference

The A=a notation always bugged me too. I like the above notation because it betrays morphism composition.

If we consider random variables as measure(able) spaces and conditional probabilities P(B | A) as stochastic maps B -> P(A), then every element 'a' of (a countably generated) A induces a point measure -> A giving probability 1 to that event. This is the map named by do(a). But since we're composing maps, not elements, we can use an element a unambiguously to mean its point measure. Then a series of measures separated by ',' give the product measure. In the above example, let a : A (implicitly, -> A), a' : B (implicitly, -> B), M : B ~> C, Y : (A,C) ~> D, then Y(a,M(a')) is a stochastic map ~> D given by composition

EDIT: How do I ascii art?

All of this is a fancy way of saying that "potential outcome" notation conveys exactly the right information to make probabilities behave nicely.