This paper presents a method of calculating sharp bounds on the average treatment
effect using linear programming under identifying assumptions commonly
used in the literature. This new method provides a sensitivity analysis of the
identifying assumptions and missing data in an application regarding the effect
of parent’s schooling on children’s schooling. Even a mild departure from identifying
assumptions may substantially widen the bounds on average treatment
effects. Allowing for a small fraction of the data to be missing also has a large
impact on the results.