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
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959
Interpreting findings from Mendelian randomization using the MR-Egger method.
Burgess, S., & Thompson, S. G. (2017) · European Journal of Epidemiology · doi:10.1007/s10654-017-0255-x · Cited by 4,571
Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018) · BMJ · doi:10.1136/bmj.k601 · Cited by 4,404
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
Hartwig, F. P., Davey Smith, G., & Bowden, J. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx102 · Cited by 3,485
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229
Self-citationBurgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G. (2017) · Epidemiology · doi:10.1097/ede.0000000000000559 · Cited by 2,017
A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization
Bowden, J., Del Greco M, F., Minelli, C., Davey Smith, G., Sheehan, N., & Thompson, J. (2017) · Statistics in Medicine · doi:10.1002/sim.7221 · Cited by 1,887
A review of instrumental variable estimators for Mendelian randomization
Burgess, S., Small, D. S., & Thompson, S. G. (2015) · Statistical Methods in Medical Research · doi:10.1177/0962280215597579 · Cited by 1,769
Bowden, J., Fabiola Del Greco, M., Minelli, C., Smith, G. D., Sheehan, N. A., & Thompson, J. R. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw220 · Cited by 1,555
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496
“While IVW and MR-Egger use a mean-based approach to obtain an overall estimate, one way to avoid the contribution of some invalid instruments is to instead base the overall estimate on the median of the instruments (63,64).”
Guidelines for performing Mendelian randomization investigations
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273
Sanderson, E., Davey Smith, G., Windmeijer, F., & Bowden, J. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy262 · Cited by 1,085
Zhao, Q., Wang, J., Hemani, G., Bowden, J., & Small, D. S. (2020) · The Annals of Statistics · doi:10.1214/19-aos1866 · Cited by 868
Mendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841
Guidelines for performing Mendelian randomization investigations: update for summer 2023
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Kutalik, Z., Holmes, M. V., Minelli, C., et al. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.3 · Cited by 747
A robust and efficient method for Mendelian randomization with hundreds of genetic variants
Burgess, S., Foley, C. N., Allara, E., Staley, J. R., & Howson, J. M. M. (2020) · Nature Communications · doi:10.1038/s41467-019-14156-4 · Cited by 681
A comparison of robust Mendelian randomization methods using summary data
Slob, E. A. W., & Burgess, S. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22295 · Cited by 644
Haycock, P., Burgess, S., Wade, K. H., Bowden, J., Relton, C. L., & Smith, G. D. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.118216 · Cited by 595
Brumpton, B., Sanderson, E., Heilbron, K., Hartwig, F. P., Harrison, S., Vie, G. Å., Cho, Y., Howe, L. D., Hughes, A., Boomsma, D. I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17117-4 · Cited by 402
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394
A Survey of Learning Causality with Data: Problems and Methods
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2021) · ACM Computing Surveys · doi:10.1145/3397269 · Cited by 362
Xue, H., Shen, X., & Pan, W. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.05.014 · Cited by 355
Big Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325
Statistical methods for Mendelian randomization in genome-wide association studies: A review
Boehm, F. J., & Zhou, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.015 · Cited by 333
Burgess, S., Foley, C. N., & Zuber, V. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083117-021731 · Cited by 291
Pleiotropy robust methods for multivariable Mendelian randomization
Grant, A. J., & Burgess, S. (2021) · Statistics in Medicine · doi:10.1002/sim.9156 · Cited by 194
Zhao, Q., Chen, Y., Wang, J., & Small, D. S. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz142 · Cited by 188
Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates
Rees, J. M. B., Wood, A., Dudbridge, F., & Burgess, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222362 · Cited by 177
Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations
Panyard, D. J., Kim, K. M., Darst, B. F., Deming, Y. K., Zhong, X., Wu, Y., Kang, H., Carlsson, C. M., Johnson, S. C., Asthana, S., et al. (2021) · Communications Biology · doi:10.1038/s42003-020-01583-z · Cited by 185
Self-citationOn the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments
Windmeijer, F., Farbmacher, H., Davies, N., & Davey Smith, G. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1498346 · Cited by 144
van Kippersluis, H., & Rietveld, C. A. (2017) · Econometrics Journal · doi:10.1111/ectj.12113 · Cited by 146
G = E: What GWAS Can Tell Us about the Environment
Gage, S. H., Davey Smith, G., Ware, J. J., Flint, J., & Munafò, M. R. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1005765 · Cited by 132
Yuan, Z., Zhu, H., Zeng, P., Yang, S., Sun, S., Yang, C., Liu, J., & Zhou, X. (2020) · Nature Communications · doi:10.1038/s41467-020-17668-6 · Cited by 129
Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., Huang, H., Ding, P., Miao, W., & Jiang, Z. (2020) · Engineering · doi:10.1016/j.eng.2019.08.016 · Cited by 147
Pleiotropy-robust Mendelian randomization.
van Kippersluis, H., & Rietveld, C. A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx002 · Cited by 125
Hu, X., Zhao, J., Lin, Z., Wang, Y., Peng, H., Zhao, H., Wan, X., & Yang, C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106858119 · Cited by 132
Allard, C., Desgagné, V., Patenaude, J., Lacroix, M., Guillemette, L., Battista, M., Doyon, M., Ménard, J., Jl, A., Perron, P., et al. (2015) · Epigenetics · doi:10.1080/15592294.2015.1029700 · Cited by 130
Invariance, Causality and Robustness
Bühlmann, P. (2020) · Statistical Science · doi:10.1214/19-sts721 · Cited by 127
Practical Considerations for Questionable IVs
Clarke, D., & Matta, B. (2018) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1801800308 · Cited by 119
Polygenic Mendelian RandomizationPreprint
Dudbridge, F. (2020) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a039586 · Cited by 110
Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes
Walker, V. M., Davies, N. M., Hemani, G., Zheng, J., Haycock, P., Gaunt, T. R., Smith, G. D., & Martin, R. M. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15334.1 · Cited by 113
Guo, Z., Kang, H., Cai, T., & Small, D. S. (2018) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12275 · Cited by 87
Self-citationBurgess, S., Zuber, V., Gkatzionis, A., Rees, J. M., & Foley, C. N. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy080 · Cited by 88
Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D. (2017) · doi:10.1101/173682 · Cited by 85
Yang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A. (2019) · doi:10.1101/19009605 · Cited by 85
Causal Inference by using Invariant Prediction: Identification and Confidence Intervals
Peters, J., Bühlmann, P., & Meinshausen, N. (2016) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12167 · Cited by 112
Mendelian randomization and pleiotropy analysis
Zhu, X. (2020) · Quantitative Biology · doi:10.1007/s40484-020-0216-3 · Cited by 66
Cheng, Q., Yang, Y., Shi, X., Yeung, K.-F., Yang, C., Peng, H., & Liu, J. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa028 · Cited by 63
Variable Selection in Causal Inference using a Simultaneous Penalization Method
Ertefaie, A., Asgharian, M., & Stephens, D. A. (2017) · Journal of Causal Inference · doi:10.1515/jci-2017-0010 · Cited by 61
Burgess, S., Bowden, J., Dudbridge, F., & Thompson, S. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03729 · Cited by 77
Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples
Zhao, Q., Wang, J., Spiller, W., Bowden, J., & Small, D. S. (2019) · Statistical Science · doi:10.1214/18-sts692 · Cited by 56
Lawlor, D. A., Wade, K. H., Borges, M. C., Palmer, T., Hartwig, F. P., Hemani, G., & Bowden, J. (2019) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/6yzs7 · Cited by 52
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
Cheng, D., Li, J., Liu, L., Liu, J., & Le, T. D. (2023) · ACM Computing Surveys · doi:10.1145/3636423 · Cited by 52
The GENIUS Approach to Robust Mendelian Randomization Inference
Tchetgen, E. T., Sun, B., & Walter, S. (2021) · Statistical Science · doi:10.1214/20-sts802 · Cited by 49
Sheehan, N. A., & Didelez, V. (2019) · Human Genetics · doi:10.1007/s00439-019-02027-3 · Cited by 47
Doubly debiased lasso: High-dimensional inference under hidden confounding
Guo, Z., Ćevid, D., & Bühlmann, P. (2022) · The Annals of Statistics · doi:10.1214/21-aos2152 · Cited by 51
A Bayesian approach to Mendelian randomization with multiple pleiotropic variants
Berzuini, C., Guo, H., Burgess, S., & Bernardinelli, L. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy027 · Cited by 42
Inferring Causal Relationships between Risk Factors and Outcomes Using Genetic Variation
Burgess, S., Foley, C. N., & Zuber, V. (2019) · Wiley eBooks · doi:10.1002/9781119487845.ch23 · Cited by 40
Grant, A. J., & Burgess, S. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.002 · Cited by 44
Gleason, K. J., Yang, F., & Chen, L. (2021) · Genetic Epidemiology · doi:10.1002/gepi.22380 · Cited by 35
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
Yuan, J., Wu, A., Kuang, K., Li, B., Wu, R., Wu, F., & Lin, L. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3494568 · Cited by 36
Testing endogeneity with high dimensional covariates
Guo, Z., Kang, H., Cai, T., & Small, D. S. (2018) · Journal of Econometrics · doi:10.1016/j.jeconom.2018.07.002 · Cited by 34
Self-citationOn uniform asymptotic risk of averaging GMM estimators
Cheng, X., Liao, Z., & Shi, R. (2019) · Quantitative Economics · doi:10.3982/qe711 · Cited by 31
Xue, H., Shen, X., & Pan, W. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2183127 · Cited by 31
A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field
Hartley, A. E., Power, G. M., Sanderson, E. C. M., & Smith, G. D. (2022) · JBMR Plus · doi:10.1002/jbm4.10675 · Cited by 32
Yao, M., Miller, G. W., Vardarajan, B. N., Baccarelli, A., Guo, Z., & Liu, Z. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100700 · Cited by 35
Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding
Miao, W., Hu, W., Ogburn, E. L., & Zhou, X. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.2023551 · Cited by 35
Barry, C.-J. S., Liu, J., Richmond, R. C., Rutter, M. K., Lawlor, D. A., Dudbridge, F., & Bowden, J. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009703 · Cited by 29
Lee, S., & Lee, W. (2025) · Journal of Obesity & Metabolic Syndrome · doi:10.7570/jomes24031 · Cited by 28
Bucur, I. G., Claassen, T., & Heskes, T. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280219851817 · Cited by 30
Robust inference in summary data Mendelian randomisation via the zero modal pleiotropy assumptionPreprint
Hartwig, F. P., Smith, G. D., & Bowden, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/126102 · Cited by 30
Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables
Cheng, D., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3133337 · Cited by 30
MRBEE: A bias-corrected multivariable Mendelian randomization method
Lorincz‐Comi, N. J., Yang, Y., Li, G., & Zhu, X. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100290 · Cited by 31
Kang, H., & Imbens, G. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04464 · Cited by 42
Self-citationConstrained instruments and their application to Mendelian randomization with pleiotropy
Jiang, L., Oualkacha, K., Didelez, V., Ciampi, A., Rosa‐Neto, P., Benedet, A. L., Mathotaarachchi, S., Richards, J. B., Greenwood, C. M. T., & Initiative, A. F. T. A. D. N. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22184 · Cited by 23
Ill-posed estimation in high-dimensional models with instrumental variables
Breunig, C., Mammen, E., & Simoni, A. (2020) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.04.043 · Cited by 23
Dixon, P., Smith, G. D., von Hinke, S., Davies, N. M., & Hollingworth, W. (2016) · PharmacoEconomics · doi:10.1007/s40273-016-0432-x · Cited by 20
Structural Equation Model Averaging: Methodology and Application
Seng, L. L., & Li, J. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2020.1870479 · Cited by 22
Mendelian randomisation approaches to the study of prenatal exposures: A systematic review
Diemer, E. W., Labrecque, J. A., Neumann, A., Tiemeier, H., & Swanson, S. A. (2021) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.12691 · Cited by 21
A bidirectional Mendelian randomized study of classical blood lipids and venous thrombosis
Lin, L., Luo, P., Yang, M., Wang, J., Hou, W., & Xu, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-31067-z · Cited by 21
Dai, J. Y., Peters, U., Wang, X., Kocarnik, J., Chang-Claude, J. C., Slattery, M. L., Chan, A. T., Lemire, M., Berndt, S. I., Casey, G., et al. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy177 · Cited by 21
Instrumental Variables in Causal Inference and Machine Learning: A Survey
Wu, A., Kuang, K., Xiong, R., & Wu, F. Q. (2025) · ACM Computing Surveys · doi:10.1145/3735969 · Cited by 28
Weak‐instrument robust tests in two‐sample summary‐data Mendelian randomization
Wang, S., & Kang, H. (2021) · Biometrics · doi:10.1111/biom.13524 · Cited by 22
Self-citationDiemer, E. W., Labrecque, J., Tiemeier, H., & Swanson, S. A. (2020) · Epidemiology · doi:10.1097/ede.0000000000001126 · Cited by 19
Social-Science Genomics: Progress, Challenges, and Future Directions
Benjamin, D., Cesarini, D., Turley, P., & Young, A. S. (2024) · doi:10.3386/w32404 · Cited by 20
Bayesian variable selection with a pleiotropic loss function in Mendelian randomization
Gkatzionis, A., Burgess, S., Conti, D. V., & Newcombe, P. J. (2021) · Statistics in Medicine · doi:10.1002/sim.9109 · Cited by 19
Causal Inference with Conditional Instruments Using Deep Generative Models
Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25869 · Cited by 19
The GENIUS Approach to Robust Mendelian Randomization InferencePreprint
Tchetgen, E. J. T., Sun, B., & Walter, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/193953 · Cited by 19
INFERENCE IN INSTRUMENTAL VARIABLE MODELS WITH HETEROSKEDASTICITY AND MANY INSTRUMENTS
Crudu, F., Mellace, G., & Sándor, Z. (2020) · Econometric Theory · doi:10.1017/s026646662000016x · Cited by 18
Considering strategies for SNP selection in genetic and polygenic risk scores
St.-Pierre, J., Zhang, X., Lu, T., Jiang, L., Loffree, X., Wang, L., Bhatnagar, S., Greenwood, C. M. T., & CANSSI team on Improving Robust High-Dimensional Causal Inference and Prediction Modelling (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.900595 · Cited by 18
Kang, H., Jiang, Y., Zhao, Q., & Small, D. S. (2021) · Observational Studies · doi:10.1353/obs.2021.0029 · Cited by 18
Self-citationThe effect of education on spousal education: A genetic approach
Barban, N., Cao, E. D., Oreffice, S., & Quintana‐Domeque, C. (2021) · Labour Economics · doi:10.1016/j.labeco.2021.102023 · Cited by 17
Estimating Individualized Causal Effect with Confounded Instruments
Wang, H., Yang, W., Yang, L., Wu, A., Xu, L., Ren, J., Wu, F. Q., & Kuang, K. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539335 · Cited by 17
The Value of Virtual Engagement: Evidence from a Running Platform
Liu, C., Wang, W., Gao, G. G., & Agarwal, R. (2020) · Management Science · doi:10.1287/mnsc.2023.4945 · Cited by 16
Davies, N. M., Hemani, G., Timpson, N. J., Windmeijer, F., & Smith, G. D. (2015) · Scientific Reports · doi:10.1038/srep16509 · Cited by 16
Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data
Cheng, D., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3262848 · Cited by 17
Swanson, S. A. (2016) · Epidemiology · doi:10.1097/ede.0000000000000558 · Cited by 16
Relationship between smoking and ALS: Mendelian randomisation interrogation of causality
Opie-Martin, S., Wootton, R. E., Budu‐Aggrey, A., Shatunov, A., Jones, A. R., Iacoangeli, A., Khleifat, A. A., Smith, G. D., & Al‐Chalabi, A. (2020) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2020-323316 · Cited by 15
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