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.