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Bounding average treatment effects using linear programming
Názov Bounding average treatment effects using linear programming Aut.údaje Lukáš Lafférs Autor Lafférs Lukáš 1986- (100%) UMBFP10 - Katedra matematiky
Zdroj.dok. Empirical Economics. Vol. 57, no. 3 (2019), pp. 727-767. - Heidelberg : Physica-Verlag, 2019 Kľúč.slová lineárne programovanie - linear programming analýza citlivosti čiastočná identifikácia Form.deskr. články - journal articles Jazyk dok. angličtina Krajina Nemecko Anotácia 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 URL Link na plný text Kategória publikačnej činnosti ADM Číslo archívnej kópie 45876 Kategória ohlasu TORGOVITSKY, Alexander. Nonparametric inference on state dependence in unemployment. In Econometrica. ISSN 0012-9682, 2019, vol. 87, no. 5, pp. 1475-1505.
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