Empir Econ https://doi.org/10.1007/s00181-018-1474-z Bounding average treatment effects using linear programming Lukáš Lafférs Received: 19 May 2017 / Accepted: 20 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 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 JEL Classiﬁcation C4 · C6 · I2 1 Introduction and literature review Different identifying strategies may lead to different conclusions, even if applied to the same data. As a solution to these divergent results, the bounding approach is becoming increasingly popular. Nevertheless, bounds are
Empirical Economics – Springer Journals
Published: Jun 4, 2018
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