 It treats causal questions as problems of missing data and gives incorrect answers to even standard scenarios.In the context of causal models, potential outcomes are interpreted causally, rather than statistically.The first law of causal inference states that the potential outcome 

can be computed by modifying causal model M (by deleting arrows into X) and computing the outcome for some "x".