Bayesian Nonparametric Models for Treatment Effect Heterogeneity: Model Parameterization, Prior Choice, and Posterior Summarization
[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022
Location: SLMath: Online/Virtual
Bayesian Nonparametric Models For Treatment Effect Heterogeneity: Model Parameterization, Prior Choice, And Posterior Summarization
Bayesian nonparametric models are a popular and effective tool for inferring the heterogeneous effects of interventions. I will discuss how to carefully specify models and prior distributions to apply judicious regularization of heterogeneous effects. I will also discuss how to extract answers to scientific and policy questions from a fitted nonparametric model using posterior summarization to avoid problems incurred by using competing or incompatible model specifications for targeting different estimands. Together these tools provide a general recipe for obtaining stable, generalizable and transferrable insights about heterogeneous effects.
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Bayesian Nonparametric Models for Treatment Effect Heterogeneity: Model Parameterization, Prior Choice, and Posterior Summarization
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Bayesian Nonparametric Models For Treatment Effect Heterogeneity: Model Parameterization, Prior Choice, And Posterior Summarization
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