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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.
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“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
A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments
Stock, J. H., Wright, J. H., & Yogo, M. (2002) · Journal of Business and Economic Statistics · doi:10.1198/073500102288618658 · Cited by 4,151
Sparse Approximate Solutions to Linear Systems
Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,866
Donoho, D. L. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20132 · Cited by 2,474
Variation in FTO contributes to childhood obesity and severe adult obesity
Dina, C., Meyre, D., Gallina, S., Durand, E., Körner, A., Jacobson, P., Carlsson, L. M. S., Kieß, W., Vatin, V., Lecœur, C., et al. (2007) · Nature Genetics · doi:10.1038/ng2048 · Cited by 1,648
The medical care costs of obesity: An instrumental variables approach
Cawley, J., & Meyerhoefer, C. D. (2011) · Journal of Health Economics · doi:10.1016/j.jhealeco.2011.10.003 · Cited by 1,517
Just relax: convex programming methods for identifying sparse signals in noise
Tropp, J. A. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.864420 · Cited by 1,385
Thorleifsson, G., Walters, G. B., Gudbjartsson, D. F., Steinthorsdottir, V., Sulem, P., Helgadottir, A., Styrkarsdottir, U., Gretarsdottir, S., Thorlacius, S., Jonsdottir, I., et al. (2009) · Nature Genetics · doi:10.1038/ng.274 · Cited by 1,375
Angrist, J. D., & Imbens, G. W. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476535 · Cited by 1,346
Mendelian randomization: prospects, potentials, and limitations
Smith, G. D. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh132 · Cited by 1,222
Avoiding Invalid Instruments and Coping with Weak Instruments
Murray, M. P. (2006) · The Journal of Economic Perspectives · doi:10.1257/jep.20.4.111 · Cited by 1,094
Pleiotropy in complex traits: challenges and strategies
Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3461 · Cited by 1,085
Utility approach to measuring health-related quality of life
Torrance, G. W. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90019-1 · Cited by 1,082
Alternative methods for evaluating the impact of interventions
Heckman, J. J., & Robb, R. A. (1985) · Journal of Econometrics · doi:10.1016/0304-4076(85)90139-3 · Cited by 1,025
Instruments for Causal Inference: An Epidemiologist's Dream?
Hernán, M. A., & Robins, J. M. (2006) · Epidemiology · doi:10.1097/01.ede.0000222409.00878.37 · Cited by 961
Mendelian randomization as an instrumental variable approach to causal inference
Didelez, V., & Sheehan, N. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206077743 · Cited by 923
Angrist, J. D., & Imbens, G. W. (1995) · Journal of the American Statistical Association · doi:10.2307/2291054 · Cited by 917
Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
Belloni, A., Chen, D. L., Chernozhukov, V., & Hansen, C. (2012) · Econometrica · doi:10.3982/ecta9626 · Cited by 892
Rubin, D. B. (1990) · Statistical Science · doi:10.1214/ss/1177012032 · Cited by 665
The lasso problem and uniqueness
Tibshirani, R. J. (2013) · Electronic Journal of Statistics · doi:10.1214/13-ejs815 · Cited by 514
The Estimation of Wage Gains and Welfare Gains in Self-Selection Models
Björklund, A., & Moffitt, R. (1987) · The Review of Economics and Statistics · doi:10.2307/1937899 · Cited by 460
APOUPOPROTEIN E ISOFORMS, SERUM CHOLESTEROL, AND CANCER
Katan, M. (1986) · The Lancet · doi:10.1016/s0140-6736(86)92972-7 · Cited by 371
C-reactive protein and its role in metabolic syndrome: mendelian randomisation study
Timpson, N. J., Lawlor, D. A., Harbord, R. M., Gaunt, T. R., Day, I. N. M., Palmer, L. J., Hattersley, A. T., Ebrahim, S. B. J., Lowe, G. D. O., Rumley, A., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67786-0 · Cited by 345
Consistent Moment Selection Procedures for Generalized Method of Moments Estimation
Andrews, D. W. K. (1999) · Econometrica · doi:10.1111/1468-0262.00036 · Cited by 333
Causal Inference, Path Analysis, and Recursive Structural Equations Models
Holland, P. W. (1988) · Sociological Methodology · doi:10.2307/271055 · Cited by 308
Glymour, M. M., Tchetgen Tchetgen, E. J., & Robins, J. M. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kwr323 · Cited by 292
Sach, T., Barton, G., Doherty, M., Muir, K. R., Jenkinson, C., & Avery, A. J. (2006) · International Journal of Obesity · doi:10.1038/sj.ijo.0803365 · Cited by 264
Hwang, J., Tsai, S., Hong, C., Yang, C.-H., Lirng, J., & Yang, Y.-M. (2005) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.10.013 · Cited by 253
Commentary: The concept of 'Mendelian Randomization'
Thomas, D. C. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh048 · Cited by 217
Inference on Structural Parameters in Instrumental Variables Regression with Weak Instruments
Wang, J., & Zivot, E. W. (1998) · Econometrica · doi:10.2307/2999621 · Cited by 178
Statistical Inference in Dynamic Economic Models
Wold, H., & Koopmans, T. C. (1951) · Econometrica · doi:10.2307/1907471 · Cited by 175
ACDC/Adiponectin Polymorphisms Are Associated With Severe Childhood and Adult Obesity
Bouatia‐Naji, N., Meyre, D., Lobbens, S., Séron, K., Fumeron, F., Balkau, B., Heude, B., Jouret, B., Scherer, P. E., Christian Dina, C., et al. (2006) · Diabetes · doi:10.2337/diabetes.55.02.06.db05-0971 · Cited by 167
Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions
Small, D. S. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000608 · Cited by 148
Allele-specific differential expression of a common adiponectin gene polymorphism related to obesity
Yang, W., Tsou, P.-L., Lee, W., Tseng, D.-L., Chen, C., Peng, C.-C., Lee, K.-C., Chen, M.-J., Huang, C., Tai, T. Y., et al. (2003) · Journal of Molecular Medicine · doi:10.1007/s00109-002-0409-4 · Cited by 146
High-dimensional instrumental variables regression and confidence setsPreprint
Gautier, É., & Rose, C. D. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1105.2454 · Cited by 126
RYBAKOWSKI, J. K., Borkowska, A., Skibińska, M., Szczepankiewicz, A., Kapelski, P., Leszczyńska-Rodziewicz, A., Czerski, P. M., & Hauser, J. (2006) · Psychiatry and Clinical Neurosciences · doi:10.1111/j.1440-1819.2006.01462.x · Cited by 123
Introductory Remarks on Neyman (1923)
Speed, T. P. (1990) · Statistical Science · doi:10.1214/ss/1177012030 · Cited by 116
Price, R. A., Li, W., & Zhao, H. (2008) · BMC Medical Genetics · doi:10.1186/1471-2350-9-4 · Cited by 111
Mutations in the adiponectin gene in lean and obese subjects from the Swedish obese subjects cohort
Ukkola, O. H., Ravussin, É., Jacobson, P., Sjöström, L., & Bouchard, C. (2003) · Metabolism · doi:10.1016/s0026-0495(03)00074-x · Cited by 92
Shugart, Y. Y., Chen, L., Day, I. N. M., Lewis, S. J., Timpson, N. J., Yuan, W., Abdollahi, M. R., Ring, S. M., Ebrahim, S. B. J., Golding, J., et al. (2009) · European Journal of Human Genetics · doi:10.1038/ejhg.2008.272 · Cited by 90
‘Mendelian randomization’ equals instrumental variable analysis with genetic instruments
Wehby, G. L., Ohsfeldt, R. L., & Murray, J. C. (2008) · Statistics in Medicine · doi:10.1002/sim.3255 · Cited by 90
Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance
Mealli, F., & Pacini, B. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.802238 · Cited by 84
Adiponectin SNP276 is associated with obesity, the metabolic syndrome, and diabetes in the elderly
Yang, W., Yang, Y., Chen, C., Wu, I.-J. U., Lu, J., Lu, F.-H., Tai, T., & Chang, C.-J. (2007) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/86.2.509 · Cited by 82
Compressed Sensing and Affine Rank Minimization Under Restricted Isometry
Cai, T., & Zhang, A. R. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2259164 · Cited by 79
Survey Response in the Long Run: The Wisconsin Longitudinal Study
Hauser, R. M. (2004) · Field Methods · doi:10.1177/1525822x04272452 · Cited by 74
Mendelian randomisation: a new spin or real progress?
Little, J., & Khoury, M. J. (2003) · The Lancet · doi:10.1016/s0140-6736(03)14396-6 · Cited by 70
The health status of obese individuals in Canada
Trakas, K., Oh, P., Singh, S., Risebrough, N. A., & Shear, N. (2001) · International Journal of Obesity · doi:10.1038/sj.ijo.0801591 · Cited by 62
A Note on the Comparison of Ordinary and Two-Stage Least Squares Estimators
Richardson, D. H., & Wu, D.-M. (1971) · Econometrica · doi:10.2307/1909670 · Cited by 50
Commentary: Mendelian randomization and gene-environment interaction
Brennan, P. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh033 · Cited by 33
Identification and Inference with Many Invalid Instruments
Kolesár, M., Chetty, R., Friedman, J. N., Glaeser, E. L., & Imbens, G. W. (2011) · National Bureau of Economic Research · doi:10.3386/w17519 · Cited by 26
Martínez-Calleja, A., Quiróz-Vargas, I., Parra‐Rojas, I., Muñóz-Valle, J. F., Leyva‐Vázquez, M. A., Fernández‐Tilapa, G., Vences-Velázquez, A., Cruz, M., Salazar-Martı́nez, E., & Flores‐Alfaro, E. (2012) · Experimental Diabetes Research · doi:10.1155/2012/982683 · Cited by 22
Beckers, S., Peeters, A. V., de Freitas, F., Mertens, I. L., Verhulst, S. L., Haentjens, D., Desager, K., Gaal, L. F. V., & Hul, W. V. (2009) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2009.00532.x · Cited by 14
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.)
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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
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
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