Bayesian Causal Inference: A Tutorial
Strategy 1: Data Augmentation (Gibbs Sampling) I Imputation crucially depends onthe model for science: Pr(Yi(1);Yi(0)jXi) I But Yi(1);Yi(0) are never jointed observed, no information at all about the association between Yi(1) an Yi(0) ! posterior = prior, and posterior of estimand ˝will be sensitive to its prior
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