Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects

Lina Zhang, David T. Frazier, Donald Poskitt and Xueyan Zhao

Papers from arXiv.org

Abstract: This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models. We decompose the ATE identification gains into components of contributions driven by IV relevancy, IV strength, direction and degree of treatment endogeneity, and matching via exogenous covariates. Our decomposition is demonstrated with graphical illustrations, simulation studies and an empirical example of childbearing and women's labour supply. Our analysis offers insights for understanding the complex role of IVs in ATE identification and for selecting IVs in practical policy designs. Simulations also suggest potential uses of our analysis for detecting irrelevant instruments.

Date: 2020-09, Revised 2022-09
New Economics Papers: this item is included in nep-ecm
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http://arxiv.org/pdf/2009.02642 Latest version (application/pdf)

Related works:
Working Paper: Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects (2021)
Working Paper: Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.02642

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