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This paper uses factor models to identify and estimate distributions of counterfactuals. We extend LISREL frameworks to a dynamic treatment effect setting, extending matching to account for unobserved conditioning variables. Using these models, we can identify all pairwise and joint treatment effects. We apply these methods to a model of schooling and determine the intrinsic uncertainty facing agents at the time they make their decisions about enrollment in school. Reducing uncertainty in returns raises college enrollment. We go beyond the "Veil of Ignorance" in evaluating educational policies and determine who benefits and who loses from commonly proposed educational reforms. We consider the information on these individuals from age 19 to age 35. For our analysis, we use the random sample of the NLSY and restrict the sample to 1161 white males for whom we have information on schooling, several parental background variables, test scores and behavior.