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

Lina Zhang, David T. Frazier, D. S. Poskitt, Xueyan Zhao

arXiv 6 Sep 2020 · Econometrics · publishedEconometric Reviews (2025) · 1 citations (OpenAlex)

arXiv:2009.02642 · PDF · DOI · OpenAlex · Extracted main text

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.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Mourifié, I (2015) Sharp bounds on treatment effects in a binary triangular system1.000105100%
2Heckman, J. J. and E. Vytlacil (2001) Instrumental variables, selection models, and tight bounds on the average treatment effect, in1.000104100%
3Chesher, A (2010) Instrumental variable models for discrete outcomes1.00084100%
4Heckman, J. J., S. Urzua, and E. Vytlacil (2006) Understanding instrumental variables in models with essential heterogeneity1.00063100%
5Vytlacil, E. and N. Yildiz (2007) Dummy endogenous variables in weakly separable models1.00053100%
6Bhattacharya, J., A. M. Shaikh, and E. Vytlacil (2012) Treatment effect bounds: An application to Swan–Ganz catheterization0.92843100%
7Manski, C. F (1990) Nonparametric bounds on treatment effects0.92843100%
8Shaikh, A. M. and E. J. Vytlacil (2011) Partial identification in triangular systems of equations with binary dependent variables0.92419679%
9Chiburis, R. C (2010) Semiparametric bounds on treatment effects0.87452100%
10Angrist, J. and W. Evans (1998) Children and their parents' labor supply: Evidence from exogenous variation in family size0.81142100%

Showing the top 10 of 59 scored citations.