arXiv 15 Mar 2022 · Econometrics · publishedJournal of Econometrics (2025)
arXiv:2203.08050 · PDF · DOI · OpenAlex · Extracted main text
Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and provides estimates of the LATEs for all remaining pairs, which can be aggregated into a single parameter of interest using researcher-specified weights. We show that the proposed VSIV estimators are asymptotically normal under weak conditions and remove or reduce the asymptotic bias relative to standard LATE estimators (that is, LATE estimators that do not use testable implications to remove invalid variation). We evaluate the finite sample properties of VSIV estimation in application-based simulations and apply our method to estimate the returns to college education using parental education as an instrument.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Noack, C (2021) Sensitivity of LATE estimates to violations of the monotonicity assumption | 0.941 | 6 | 4 | 83% |
| 2 | Li, L., Kédagni, D., and Mourifié, I (2024) Discordant relaxations of misspecified models | 0.928 | 15 | 5 | 80% |
| 3 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.885 | 13 | 5 | 69% |
| 4 | Mourifié, I. and Wan, Y (2017) Testing local average treatment effect assumptions | 0.874 | 12 | 6 | 67% |
| 5 | Mogstad, M., Torgovitsky, A., and Walters, C. R (2021) The causal interpretation of two-stage least squares with multiple instrumental variables | 0.874 | 11 | 2 | 100% |
| 6 | Frandsen, B., Lefgren, L., and Leslie, E (2023) Judging judge fixed effects | 0.874 | 5 | 2 | 100% |
| 7 | Kitagawa, T (2015) A test for instrument validity | 0.862 | 25 | 10 | 64% |
| 8 | Kédagni, D (2023) Identifying treatment effects in the presence of confounded types | 0.843 | 5 | 4 | 60% |
| 9 | Huber, M (2014) Sensitivity checks for the local average treatment effect | 0.843 | 3 | 3 | 100% |
| 10 | Cui, Y., Kédagni, D., and Wu, H (2024) Robust identification in randomized experiments with noncompliance | 0.794 | 6 | 4 | 50% |
Showing the top 10 of 69 scored citations.
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