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Treatment effects with targeting instruments
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DOI：10.1016/j.jeconom.2026.106253.png)
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Abstract
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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 explore the additional identifying power of a positive selection assumption. We illustrate its usefulness by revisiting the findings of Kline and Walters (2016) on the Head Start Impact Study. We derive informative bounds that suggest less beneficial effects of Head Start expansions than their parametric estimates.
Keywords:
multivalued treatments
discrete-valued instruments
selection bias
counterfactual averages
treatment effects
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