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Workshop: The Neglected Assumptions In Causal Inference

Models, identifiability, and estimability in causal inference

Oliver Maclaren


Here we discuss two common but, in our view, misguided assumptions in causal inference. The first assumption is that one requires potential outcomes, directed acyclic graphs (DAGs), or structural causal models (SCMs) for thinking about causal inference in statistics. The second is that identifiability of a quantity implies estimability of that quantity. These views are not universal, but we believe they are sufficiently common to warrant comment.

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