Andrew Gelman is giving a talk on 11 October at 2 p.m. in Campus Pierre et Marie Curie (Sorbonne Université), room 16-26-209. He will talk about

**Prior distribution for causal inference**

In Bayesian inference, we must specify a model for the data (a likelihood) and a model for parameters (a prior). Consider two questions:

- Why is it more complicated to specify the likelihood than the prior?
- In order to specify the prior, how could can we switch between the theoretical literature (invariance, normality assumption, …) and the applied literature (experts elicitation, robustness, …)?

I will discuss those question in the domain of causal inference: prior distributions for causal effects, coefficients of regression and the other parameters in causal models.

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