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Bayesian Causal Inference by Prof. Peng Ding, University of California Berkeley

Thu, April 25th, 2024
11:20 am
- 12:35 pm

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Bayesian Causal Inference by Prof. Peng Ding, University of California Berkeley, Thursday April 25, 11:20am – 12:35pm, North Science Building 015, Wachenheim, Class of 1960 Scholar

Abstract: I will give an overview of Bayesian causal inference. Under the potential outcomes framework, each unit has multiple potential outcomes but the researcher can only observe one of them. Therefore, causal inference can be viewed as a missing data problem. Under the Bayesian paradigm, it is natural to deal with missing data by imputing them based on their posterior predictive distributions. Consequently, it is natural to infer causal effects by imputing the missing potential outcomes based on their posterior predictive distributions. I will illustrate this idea with some canonical examples in causal inference.

 

Lunch will be provided. Please RSVP through https://forms.gle/u44b6kpJVXESa6NE6 if you are interested to attend.

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