Problem
Consider the problem of Bayesian learning for a functional causal model C over a set of endogenous variables X. Assume we have a data set D where the endogenous variables X are fully observed. Describe a way for approximating the parameter posterior P(ν|X) using collapsed Gibbs sampling. Specifically, your algorithm should sample the response variables U and compute a closed-form distribution over the parameters ν.