The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index.
Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization.
Abstract
Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments’ validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments’ validity is equivalent to complete knowledge about the involved genes’ functions. In this article, we propose a method for estimation of causal effects when this complete knowledge is absent. It is shown that causal effects are identified and can be estimated as long as less than 50% of instruments are invalid, without knowing which of the instruments are invalid. We also introduce conditions for identification when the 50% threshold is violated. A fast penalized ℓ1 estimation method, called sisVIVE, is introduced for estimating the causal effect without knowing which instruments are valid, with theoretical guarantees on its performance. The proposed method is demonstrated on simulated data and a real Mendelian randomization study concerning the effect of body mass index(BMI) on health-related quality of life (HRQL) index. An R package sisVIVE is available on CRAN. Supplementary materials for this article are available online.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Variable selection and dimension reduction approaches have been proposed to maximize the proportion of variance in the exposure explained by the selected variants while avoiding instability due to multicollinearity 63 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [63]
Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
(1998) · PsycEXTRA Dataset · doi:10.1037/e565682010-001 · Cited by 9,453
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357
Decoding by Linear Programming
Candès, E. J., & Tao, T. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.858979 · Cited by 7,134
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G. (2008) · Statistics in Medicine · doi:10.1002/sim.3034 · Cited by 4,618
Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
Research works printed but not linked: 4
- National Institutes of Health, 1998, Obesity Research, 2, 51S
- Neyman J., 1923, Statistical Science, 5, 463
- Roetker N.S., 2012, British Medical Journal Open, 2, e000944
- Wooldridge J.M., 2010, Econometric Analysis of Cross Section and Panel Data (2nd ed.)
Cited by
Sorted by citations
Items 201–263 of 263
Distributed empirical likelihood inference with or without Byzantine failures
Wang, Q., Du, J., & Sheng, Y. (2025) · Statistics and Computing · doi:10.1007/s11222-025-10699-3 · Cited by 1
Distributed Estimation and Inference for High-Dimensional Confounded Models
Liu, J., Fei, Y., Ma, W., & Wang, L. (2025) · Statistics and Computing · doi:10.1007/s11222-025-10710-x · Cited by 1
Wang, Y., Qian, W., Sun, H., Zhao, J., Mu, M., & Yang, Z. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120181 · Cited by 1
Yang, Q., & Jia, J. (2025) · Statistics in Medicine · doi:10.1002/sim.70276 · Cited by 1
Model-free feature screening for ultrahigh dimensional data with responses missing not at random
Bai, Y., & Tang, N. (2026) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2026.105605 · Cited by 1
Ma, R., An, H. Y., & Feng, Y. (2026) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-25-01711 · Cited by 1
Disentangled Instrumentalization Learning for Dialogue Emotion Detection
Nie, W., Peng, Z., Wei, Y., Wang, W., & Liu, A. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3669234 · Cited by 2
Bayesian Model Averaging in Causal Instrumental Variable Models
Steiner, G., & Steel, M. (2026) · Journal of Applied Econometrics · doi:10.1002/jae.70070 · Cited by 1
Ala-Kokko, K., Tack, J., & Yu, J. (2026) · Food Policy · doi:10.1016/j.foodpol.2026.103138 · Cited by 1
Li, Y., Wong, K. Y., Howard, A. G., Gordon‐Larsen, P., Highland, H. M., Graff, M., North, K. E., Downie, C. G., Avery, C. L., Yu, B., et al. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100245 · Cited by 1
Luo, P., Gao, D., Wang, C., Guo, R. Z., & Zhang, Q. (2024) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s479500 · Cited by 1
LASSO-type instrumental variable selection methods with an application to Mendelian randomization
Qasim, M., Månsson, K., & Balakrishnan, N. (2024) · Statistical Methods in Medical Research · doi:10.1177/09622802241281035 · Cited by 2
Instrumental Variable Model Average With Applications in Nonlinear Causal Inference
Dong, C., Wang, Y., Shi, D., Cao, Y., & Hu, Y. (2024) · Statistics in Medicine · doi:10.1002/sim.10269 · Cited by 1
Endogenous treatment effect estimation using high-dimensional instruments and double selection
Zhong, W., Gao, Y., Zhou, W., & Fan, Q. (2020) · Statistics & Probability Letters · doi:10.1016/j.spl.2020.108967 · Cited by 1
Hasegawa, R. B., & Small, D. S. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1863222 · Cited by 1
Opportunities, Devices, and Instruments
Rosenbaum, P. R. (2020) · Springer series in statistics · doi:10.1007/978-3-030-46405-9_5 · Cited by 1
Kang, H. (2022) · Biometrics · doi:10.1111/biom.13784 · Cited by 1
Self-citationSummarising multiple bounds of the average causal effect in Mendelian randomisation
Kang, H. (2023) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.12973 · Cited by 1
Self-citationThe synthetic instrument: from sparse association to sparse causation
Tang, D., Kong, D., & Wang, L. (2026) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkaf083 · Cited by 1
Mendelian Randomization with Instrumental Variable Synthesis (IVY)Preprint
Kuang, Z., Córdova‐Palomera, A., Sala, F. G., Wu, S., Dunnmon, J., Ré, C., & Priest, J. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/657775 · Cited by 1
Relaxing the Exclusion Restriction in Shift-Share Instrumental Variable EstimationPreprint
Apfel, N. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3408682 · Cited by 1
On Multiply Robust Mendelian Randomization (MR 2 ) With Many Invalid Genetic InstrumentsPreprint
Sun, B., Liu, Z., & Tchetgen, E. T. (2021) · medRxiv · doi:10.1101/2021.10.21.21265317 · Cited by 1
Testing Overidentifying Restrictions with High-Dimensional Data and HeteroskedasticityPreprint
Fan, Q., Guo, Z., & Mei, Z. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4097813 · Cited by 1
Yao, M., Miller, G. W., Vardarajan, B. N., Baccarelli, A., Guo, Z., & Liu, Z. (2023) · medRxiv · doi:10.1101/2023.02.20.23286200 · Cited by 1
Instrumental Variables and Mendelian Randomization With Invalid Instruments
Kang, H. (2015) · Scholarly Commons (University of Pennsylvania)
Self-citationUniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspeci…cationPreprint
Cheng, X., Liao, Z., & Shi, R. (2015) · SSRN Electronic Journal
Silva, R., & Shimizu, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02722
Identifying and Estimating Principal Causal Effects in Multi-site TrialsPreprint
Yuan, L.-H., Feller, A., & Miratrix, L. W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06048
Distinguishing correlation from causation using genome-wide association studiesPreprint
O’Connor, L. J., & Price, A. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08803
A comparison of robust methods for Mendelian randomization using multiple genetic variants
Bao, Y., Clarke, P. B. S., Smart, M., & Meena Kumari, B. (2018) · Econstor (Econstor)
Association of Obesity with Health Care Costs: A Mendelian Randomization studyPreprint
Kurz, C. F., & Laxy, M. (2019) · bioRxiv · doi:10.1101/630178
Adaptive k-class estimation in high-dimensional linear models
Fan, Q., & Yu, W. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1634206
Bayesian causal inference with some invalid instrumental variablesPreprint
Goh, G., & Yu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07663
Relaxing the Exclusion Restriction in Shift-Share Instrumental Variable EstimationPreprint
Apfel, N. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3303397
Orihara, S. (2021) · arXiv (Cornell University)
The Virtualization of Experiential Events: Effects on Platform UsersPreprint
Liu, C., Wang, W., Gao, G. G., & Agarwal, R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3739418
de Boer, N. S. (2020) · Thesis Commons (OSF Preprints) · doi:10.31237/osf.io/c4wz5
Jayarathna, D. K. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.237470
Causal Inference with Secondary Outcomes
Zhou, Y., & Initiative, A. D. N. (2023) · Statistics in Biosciences · doi:10.1007/s12561-023-09363-z
Nonlinear Estimation Methods for Mendelian Randomization in Genetic Studies
Cho, Y., Auer, P. L., & Ghosh, D. (2023) · Sankhya B · doi:10.1007/s13571-023-00309-5
HILAMA: High-dimensional multi-omic mediation analysis with latent confoundingPreprint
Wang, X., Liu, J., Hu, S. S., Liu, Z., Lu, H. D., Liu, L. L., & Initiative, T. A. D. N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.15.557839
simmr: An open-source tool to perform simulations in Mendelian RandomizationPreprint
Lorincz‐Comi, N. J., Yang, Y., & Zhu, X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.11.556975
Penalized Semiparametric Estimation for Causal Inference with Possibly Invalid InstrumentsPreprint
Cao, Y., Wang, Y., Shi, D., Chen, D., & Hu, Y. (2024) · medRxiv · doi:10.1101/2024.01.19.24301518
Liu, Z., Yao, M., Miller, G. W., Vardarajan, B. N., Baccarelli, A., & Guo, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-3703437/v1
simmrd: An open‐source tool to perform simulations in Mendelian randomization
Lorincz‐Comi, N. J., Yang, Y., & Zhu, X. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22544 · Cited by 1
Farbmacher, H., & Mühlegger, M. (2023) · Springer eBooks · doi:10.1007/978-3-662-63496-7_22-1
Structural Barriers to the Reintegration of Returning Refugees: Evidence from Post-Conflict CambodiaPreprint
Kogure, K., & Kubo, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4712749
Statistical Methods for Mendelian Randomization
Orihara, S., & Hirano, K. (2025) · Japanese Journal of Biometrics · doi:10.5691/jjb.46.1
Communication-Efficient Oracle Estimation for Distributed Instrumental Variables RegressionPreprint
Chen, Y., Hu, H., Jin, J., Yang, S., & Zhong, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5399809
Oreshkov, S., Thorball, C. W., Maylan, J., Muriset, M., Calmy, A., Ciuffi, A., Elzi, L., Nemeth, J., Muhammad, A., Baumann, L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688267
Huang, Z., Huang, J., Leung, C. K., Zhang, C. J., Akinwunmi, B. O., & Ming, W. (2024) · Figshare · doi:10.6084/m9.figshare.25449055
Edwards, C. H., Vie, G. Å., & Kinge, J. M. (2022) · Figshare · doi:10.6084/m9.figshare.19425066
Chen, D., Wang, Y., Shi, D. H., Cao, Y., & Hu, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf714
A regression-based approach for bidirectional proximal causal inference
Min, J., Zhang, X., & Luo, S. (2026) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnag031
Two-stage least squares model averaging for instrumental variable models with exogenous variables
Shen, W., & Xia, X. (2026) · Statistical Theory and Related Fields · doi:10.1080/24754269.2026.2635747
Marginal Treatment Effects in the Absence of Instrumental Variables
Pan, Z., Wang, Z., Zhang, J., & Zhou, Y. (2026) · Journal of Applied Econometrics · doi:10.1002/jae.70052 · Cited by 2
Declining Business Dynamism: The Heterogeneous Role of Market Power *Preprint
Tiwari, A. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6298806
Cao, Y., Wang, Y., Shi, D. H., Chen, D., & Hu, Y. (2026) · Statistical Methods in Medical Research · doi:10.1177/09622802261439252
Orihara, S., Imori, S., Morikawa, K., Goto, A., & Taguri, M. (2026) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-026-00985-w
Doubly robust identification for bivariate causal discovery under unmeasured confounding
Li, W., Duan, R., & Li, S. W. (2026) · Biometrika · doi:10.1093/biomet/asag035
Online inference under over-parameterized models with hidden confounders
Chen, S., Feng, X., Ge, Y., Li, T., & Wu, M. (2026) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkag110
Causal estimation of interval-censored failure time data with a binary instrumental variable
Lou, Y., Du, M., Wang, P., & Song, X. (2026) · Test · doi:10.1007/s11749-026-01031-w
Metadata
- Authors
- Kang, H., Zhang, A., Cai, T. T., & Small, D. S.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2016-01-02
- Volume
- 111
- Issue
- 513
- Pages
- 132-144
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1991587639
- Cited by
- 265
- References
- 62
- Citations by year
- 2026: 16 · 2025: 20 · 2024: 28 · 2023: 27 · 2022: 29 · 2021: 18 · 2020: 35 · 2019: 26 · 2018: 26 · 2017: 14 · 2016: 16
- FWCI
- 19.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- ScholarlyCommons (University of Pennsylvania) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- instrumental variables, invalid instruments, Mendelian randomization, causal effect estimation, causal identification, genetic instruments, body mass index, health-related quality of life, unmeasured confounding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review