EconBase
← All papers

Breakdown Analysis for Instrumental Variables with Binary Outcomes

Pedro Picchetti

arXiv 14 Jul 2025 · Econometrics · 1 citations (OpenAlex)

arXiv:2507.10242 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies the partial identification of treatment effects in Instrumental Variables (IV) settings with binary outcomes under violations of independence. I derive the identified sets for the treatment parameters of interest in the setting, as well as breakdown values for conclusions regarding the true treatment effects. I derive $\sqrt{N}$-consistent nonparametric estimators for the bounds of treatment effects and for breakdown values. These results can be used to assess the robustness of empirical conclusions obtained under the assumption that the instrument is independent from potential quantities, which is a pervasive concern in studies that use IV methods with observational data. In the empirical application, I show that the conclusions regarding the effects of family size on female unemployment using same-sex siblings as the instrument are highly sensitive to violations of independence.

Citation extraction

22
references
38
in-text mentions
22
distinct cited
0
self-citations
8,682
main-text words

appendix boundary found by appendix_titled_section at “Appendix A” · 44% of the source is main text. Read the extracted text to check this.

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
1Masten, M. A. and Poirier, A (2018) Identification of treatment effects under conditional partial independence0.9416483%
2Kline, P. and Santos, A (2013) Sensitivity to missing data assumptions: Theory and an evaluation of the u.s. wage structure0.92843100%
3Masten, M. A. and Poirier, A (2020) Inference on breakdown frontiers0.8435360%
4Fang, Z. and Santos, A (2018) Inference on directionally differentiable functions0.7373367%
5Angrist, J. D. and Evans, W. N (1998) Children and their parents' labor supply: Evidence from exogenous variation in family size0.64422100%
6Rambachan, A. and Roth, J (2025) Design-based uncertainty for quasi-experiments0.64422100%
7Machado, C., Shaikh, A. M., and Vytlacil, E. J (2019) Instrumental variables and the sign of the average treatment effect0.40511100%
8Altonji, J. G., Elder, T. E., and Taber, C. R (2008) Using selection on observed variables to assess bias from unobservables when evaluating swan-ganz catheterization0.40511100%
9Chesher, A. and Rosen, A. M (2013) What do instrumental variable models deliver with discrete dependent variables?0.40511100%
10Cinelli, C. and Hazlett, C (2025) An omitted variable bias framework for sensitivity analysis of instrumental variables0.40511100%

Showing the top 10 of 22 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1The Markup Falsification Adaptive Set0.40511