Matching estimators use observed variables to adjust for differences between groups to eliminate sample selection bias. When minimum relevant information is not available, matching estimates are biased. If access to data on usually unobserved factors that determine the selection process is unavailable, other estimators should be used. This study advocates the one-factor control function estimator that allows for unobserved heterogeneity with factor-loading technique. Treatment effects of vocational training in Sweden are estimated with mean and distributional parameters, and then compared with matching estimates. The results indicate that unobservables slightly increase the treatment effect for those treated.