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

Lina Zhang (), David Frazier (), Donald Poskitt and Xueyan Zhao

No 21/21, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: heterogeneous treatment effect; binary dependent variables; propensity score; asymmetric endogeneity; instrument identification power (search for similar items in EconPapers)
JEL-codes: C14 C31 C35 C36 (search for similar items in EconPapers)
Pages: 56
Date: 2021
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.monash.edu/business/ebs/research/publications/ebs/wp21-2021.pdf (application/pdf)

Related works:
Working Paper: Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects (2022)
Working Paper: Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2021-21

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().