An empirical Bayes method aggregates estimators from multiple identification functionals for a causal effect, establishing consistency under exact identifiability or growing mean-zero bias regimes while using a working independence device for dependent estimators.
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Mixture priors enable flexible Bayesian pooling of original and replication data via a mixture weight, with Bayes factors for testing effects and pooling levels, shown on three studies with an R package.
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Shrinkage through multiple identifiability
An empirical Bayes method aggregates estimators from multiple identification functionals for a causal effect, establishing consistency under exact identifiability or growing mean-zero bias regimes while using a working independence device for dependent estimators.
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Mixture priors for replication studies
Mixture priors enable flexible Bayesian pooling of original and replication data via a mixture weight, with Bayes factors for testing effects and pooling levels, shown on three studies with an R package.