Bounding average treatment effects using linear programming

Lukas Laffers

Empirical Economics, 2019, vol. 57, issue 3, No 2, 727-767

Abstract: 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.

Keywords: Partial identification; Bounds; Average treatment effect; Sensitivity analysis; Linear programming (search for similar items in EconPapers)
JEL-codes: C4 C6 I2 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Bounding average treatment effects using linear programming (2015)
Working Paper: Bounding average treatment effects using linear programming (2015)
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DOI: 10.1007/s00181-018-1474-z

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