Andrew Heiss • 12/20/2021

How to create a(n almost) fully Bayesian outcome model with inverse probability weights

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This technical article explains how to integrate a posterior distribution of inverse probability weights into a Bayesian outcome model for causal inference. It builds on a previous post about Bayesian propensity scores, detailing the implementation in R using the brms package and Stan to avoid running thousands of separate models.

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#R #Bayesian Statistics #Causal Inference
How to create a(n almost) fully Bayesian outcome model with inverse probability weights

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