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Bounding average treatment effects using linear programming

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  • Lukáš Lafférs

    (Institute for Fiscal Studies)

Abstract

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.

Suggested Citation

  • Lukáš Lafférs, 2015. "Bounding average treatment effects using linear programming," CeMMAP working papers CWP70/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:70/15
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    File URL: https://www.ifs.org.uk/uploads/cemmap/wps/cwp701515.pdf
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    References listed on IDEAS

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    Cited by:

    1. Lukáš Lafférs, 2019. "Identification in Models with Discrete Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 657-696, February.

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    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • I2 - Health, Education, and Welfare - - Education

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