Bayesian causal computation for sample estimands requires joint posterior sampling of cross-world counterfactuals while many population estimands need only parameter posteriors, and common procedures can silently target the wrong one.
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Untangling Sample and Population Level Estimands in Bayesian Causal Computation
Bayesian causal computation for sample estimands requires joint posterior sampling of cross-world counterfactuals while many population estimands need only parameter posteriors, and common procedures can silently target the wrong one.