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Bounding average treatment effects using linear programming

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  • Lukáš Lafférs

    (Matej Bel University)

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 two applications. The first application looks at the effect of parents’ schooling on children’s schooling, and the second application studies the effect of mandatory arrest policy on domestic violence recidivism. This paper shows that 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, 2019. "Bounding average treatment effects using linear programming," Empirical Economics, Springer, vol. 57(3), pages 727-767, September.
    • Handle: RePEc:spr:empeco:v:57:y:2019:i:3:d:10.1007_s00181-018-1474-z
    DOI: 10.1007/s00181-018-1474-z
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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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    More about this item

    Keywords

    Partial identification; Bounds; Average treatment effect; Sensitivity analysis; Linear programming;
    All these keywords.

    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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