In many settings, only observational data is available for estimating the effects of treatments.  Observational data, however, contains many unobserved confounders that can make reliable estimation quite difficult.  This talk will considered approaches that use subsets of the observational data with revealed confounders in conjunction with partial information on data without confounder information to estimate overall treatment effects.  The talk will describe conditions favoring different approaches and solutions of the resulting constrained optimization problems.  The approaches will be illustrated using a variety of simulated and empirical sample datasets.