nNo missing data or hidden variables
n
nLet G be a causal graph then:
n
nCausal Markov Assumption
nData is generated according a Bayesian Network with structure G
nE.g., Can’t have G=(X
Y) and X dependent on Y
n
nFaithfulness Assumption
nNo probabilistic independencies other than those represented by G
nE.g., Can’t have G=(X®Y) and X independent of Y
n
n