Treatment Effects with Targeting Instruments

2020/07/20 by Lee, Sokbae, Salanié, Bernard
#Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2007.10432

Abstract

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We illustrate the usefulness of our framework by applying it to data from the Head Start Impact Study. Under a plausible positive selection assumption, we derive informative bounds that suggest less beneficial effects of Head Start expansions than the parametric estimates of Kline and Walters (2016).

Related

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• On Recoding Ordered Treatments as Binary Indicators
• Sharp Bounds for the Marginal Treatment Effect with Sample Selection
• Lee Bounds with a Continuous Treatment in Sample Selection
• Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects