Assumptions
		n No missing data or hidden variables n Let G be a causal graph then: n Causal Markov Assumption n Data is generated according a Bayesian Network with structure G n E.g., Can’t have G=(X  Y) and X dependent on Y n Faithfulness Assumption n No probabilistic independencies other than those represented by G n E.g., Can’t have G=(X®Y) and X independent of Y