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., Canít have G=(XY) and X dependent on Y
n Faithfulness Assumption
n No probabilistic independencies other than those
represented by G
n E.g., Canít have G=(XģY) and X independent of Y