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Workshop: The Neglected Assumptions In Causal Inference

Randomization does not imply unconfoundedness

Fredrik Savje


A common assumption in causal inference is that random treatment assignment ensures that potential outcomes are independent of treatment, or in one word, unconfoundedness. This paper highlights that randomization and unconfoundedness are separate properties, and neither implies the other. A study with random treatment assignment does not have to be unconfounded, and a study with deterministic assignment can still be unconfounded. A corollary is that a propensity score is not the same thing as a treatment assignment probability. These facts should not be taken as arguments against randomization. The moral of this paper is that randomization is useful only when investigators know or can reconstruct the assignment process.

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