# On falsification of the binary instrumental variable model

arXiv: https://arxiv.org/abs/1605.03677 · HTML: https://mlsanity.com/paper/1605.03677/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2016.

## Built on by (most cited first, 5 of 5)

- [Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox](https://mlsanity.com/paper/2110.10961/) (2021)
- [On Testability of the Front-Door Model via Verma Constraints](https://mlsanity.com/paper/2203.00161/) (2022)
- [Proximal Causal Inference With Text Data](https://mlsanity.com/paper/2401.06687/) (2024)
- [Causal inference with imperfect instrumental variables](https://mlsanity.com/paper/2111.03029/) (2021)
- [Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses](https://mlsanity.com/paper/2502.10161/) (2025)

## Abstract (arXiv)

Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications on the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures for testing the binary instrumental variable model. Our methods are based on existing approaches for comparing two treatments, such as the t-test and the Gail--Simon test. We illustrate the importance of testing the instrumental variable model by evaluating the exogeneity of college proximity using the National Longitudinal Survey of Young Men.