Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper reformulates the problem of bounding average treatment effects under sample selection studied in Lee (2009) as an optimization problem. This allows researchers to easily conduct sensitivity analyses of the identifying assumptions while the bounds remain sharp. We provide a mathematical formulation of the problem, replicate the existing analytical results and extend them to a sensitivity analysis.