On the identifying content of instrument monotonicity

SUMMARY: The existing literature has established that the instrument monotonicity assumption of Imbens and Angrist (1994) has no identifying content on the average treatment effect and more generally on the marginal distributions of potential outcomes in the sense that additionally imposing this assumption does not affect the identified set for the parameters of interest under instrument exogeneity whenever these assumptions are consistent with the data. This paper demonstrates that the identifying content of instrument monotonicity presents itself in features of the joint distribution of potential outcomes. In the context of a model with a binary outcome, a binary treatment, and an exogenous binary instrument, I derive identified sets for the distribution of potential outcomes with and without instrument monotonicity. Comparing the two identified sets, I provide necessary and sufficient conditions on the distribution of the data under which the former identified set can be a strict subset of the latter. I empirically illustrate the results by revisiting the study of Angrist and Evans (1998).

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Publication Details

Journal
Econometrics Journal
Published
2025-10-30
DOI
https://doi.org/10.1093/ectj/utaf025
Citations
2
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
2.63
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article

On the identifying content of instrument monotonicity

Vishal Kamat
2 citations
Econometrics Journal
Advanced Causal Inference Techniques
2.63
article

On the identifying content of instrument monotonicity

Vishal Kamat
article en
2 citations

Abstract

SUMMARY: The existing literature has established that the instrument monotonicity assumption of Imbens and Angrist (1994) has no identifying content on the average treatment effect and more generally on the marginal distributions of potential outcomes in the sense that additionally imposing this assumption does not affect the identified set for the parameters of interest under instrument exogeneity whenever these assumptions are consistent with the data. This paper demonstrates that the identifying content of instrument monotonicity presents itself in features of the joint distribution of potential outcomes. In the context of a model with a binary outcome, a binary treatment, and an exogenous binary instrument, I derive identified sets for the distribution of potential outcomes with and without instrument monotonicity. Comparing the two identified sets, I provide necessary and sufficient conditions on the distribution of the data under which the former identified set can be a strict subset of the latter. I empirically illustrate the results by revisiting the study of Angrist and Evans (1998).

Econometrics Journal
Queen Mary University of London (GB), Toulouse School of Economics (FR)
Openalex Percentile: Top 16%
Advanced Causal Inference Techniques
2.63
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On the identifying content of instrument monotonicity — Vishal Kamat · Econometrics Journal (2025) | TGRS Research Map | TGRS