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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.
Authors
Lukáš Lafférs
Working Paper details
- DOI
- 10.1920/wp.cem.2015.7015
- Publisher
- cemmap
Suggested citation
Lafférs, L. (2015). Bounding average treatment effects using linear programming. London: cemmap. Available at: https://ifs.org.uk/publications/bounding-average-treatment-effects-using-linear-programming (accessed: 2 July 2024).
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