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What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Firstly, we show how to translate causal inference problems into an abstract statistical formalism without requiring any structure beyond an arbitrarily-indexed family of probability models.
An Additive Approximation to Multiplicative Noise
The approach is essentially a Bayesian one in that the statistics of the additive error is induced by the statistics of the other unknowns.
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