Treatment Effects with Targeting Instruments

TL;DR

This paper investigates how discrete instruments can identify treatment effects in multivalued treatment settings, emphasizing targeting and selection assumptions to improve identification and bounds.

Contribution

It introduces conditions for point and partial identification of treatment effects using targeting instruments and explores the impact of positive selection assumptions.

Findings

Derived bounds indicating less beneficial effects of Head Start than previous estimates.

Established conditions for identification of treatment effects with targeting instruments.

Revisited Head Start Impact Study with new bounds and insights.

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 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.