Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships
Open accessAbstract
The gold standard of study design for treatment evaluation is widely acknowledged to be the randomized controlled trial (RCT). Trials allow for the estimation of causal effect by randomly assigning participants either to an intervention or comparison group; through the assumption of "exchangeability" between groups, comparing the outcomes will yield an estimate of causal effect. In the many cases where RCTs are impractical or unethical, instrumental variable (IV) analysis offers a nonexperimental alternative based on many of the same principles. IV analysis relies on finding a naturally varying phenomenon, related to treatment but not to outcome except through the effect of treatment itself, and then using this phenomenon as a proxy for the confounded treatment variable. This article demonstrates how IV analysis arises from an analogous but potentially impossible RCT design, and outlines the assumptions necessary for valid estimation. It gives examples of instruments used in clinical epidemiology and concludes with an outline on estimation of effects.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
IV analyses can be applied to any potential source of randomization, including intentionally designed RCTs or quasi-randomization in natural experiments.
“IV analyses can be applied to any potential source of randomization, including intentionally designed RCTs or quasi-randomization in natural experiments.15,16”
From Mendelian randomization (2022) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Introductory Econometrics: A Modern Approach
Wooldridge, J. M. (1999) · Cited by 14,354 · see citing works
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. H. (1997) · Econometrica · doi:10.2307/2171753 · Cited by 7,081 · see citing works
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302 · cited 1× · see citing works
“With an appropriate measure of a physician’s preference, we can test whether assumption (1) holds: the strength of the association can be quantified and the assumption verified by means of goodness-of-fit measures, such as the F statistic, often cited by economists, or the partial r2 value [29,30].”
BackgroundBound, J., Jaeger, D. A., & Baker, R. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476536 · Cited by 3,845 · see citing works
Vitamin E Consumption and the Risk of Coronary Heart Disease in Men
Rimm, E. B., Stampfer, M. J., Ascherio, A., Giovannucci, E. L., Colditz, G. A., & Willett, W. C. (1993) · New England Journal of Medicine · doi:10.1056/nejm199305203282004 · Cited by 2,335 · see citing works
Hernán, M. Á., Brumback, B., & Robins, J. M. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00012 · Cited by 1,772 · see citing works
A review of uses of health care utilization databases for epidemiologic research on therapeutics
Schneeweiß, S., & Avorn, J. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.10.012 · Cited by 1,290 · see citing works
An introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156 · see citing works
Lee, I.-M., Cook, N. R., Gaziano, J. M., Gordon, D., Ridker, P. M., Manson, J. E., Hennekens, C. H., & Buring, J. E. (2005) · JAMA · doi:10.1001/jama.294.1.56 · Cited by 1,137 · see citing works
Bound, J., Jaeger, D. A., & Baker, R. M. (1995) · Journal of the American Statistical Association · doi:10.2307/2291055 · Cited by 1,018 · see citing works
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 · see citing works
Risk of Death in Elderly Users of Conventional vs. Atypical Antipsychotic Medications
Wang, P. S., Schneeweiss, S., Avorn, J., Fischer, M. A., Mogun, H., Solomon, D. H., & Brookhart, M. A. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052827 · Cited by 959 · see citing works
Self-citationEstimation of Limited Dependent Variable Models With Dummy Endogenous Regressors
Angrist, J. D. (2001) · Journal of Business and Economic Statistics · doi:10.1198/07350010152472571 · Cited by 848 · see citing works
Analysis of Observational Studies in the Presence of Treatment Selection Bias
Stukel, T. A., Fisher, E. S., Wennberg, D. E., Alter, D. A., Gottlieb, D. J., & Vermeulen, M. J. (2007) · JAMA · doi:10.1001/jama.297.3.278 · Cited by 789 · see citing works
Angrist, J. D. (2000) · National Bureau of Economic Research · doi:10.3386/t0248 · Cited by 752 · see citing works
ECONOMETRICS IN OUTCOMES RESEARCH: The Use of Instrumental Variables
Newhouse, J. P., & McClellan, M. (1998) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.19.1.17 · Cited by 678 · see citing works
What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?
Smith, G. D., & Ebrahim, S. (2005) · BMJ · doi:10.1136/bmj.330.7499.1076 · Cited by 545 · see citing works
Correcting for non-compliance in randomized trials using structural nested mean models
Robins, J. M. (1994) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929408831393 · Cited by 534 · see citing works
Brookhart, M. A., Wang, P. S., Solomon, D. H., & Schneeweiß, S. (2006) · Epidemiology · doi:10.1097/01.ede.0000193606.58671.c5 · Cited by 341 · see citing works
Self-citationMcClellan, M., McNeil, B. J., & Newhouse, J. P. (1995) · Survey of Anesthesiology · doi:10.1097/00132586-199506000-00003 · Cited by 332 · see citing works
Shapiro, S. (1997) · PubMed · Cited by 299 · see citing works
Brookhart, M. A., & Schneeweiß, S. (2007) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1072 · Cited by 221 · see citing works
Self-citationMcClellan, M. (1994) · JAMA · doi:10.1001/jama.272.11.859 · Cited by 165 · see citing works
Walker, A. M. (1996) · PubMed · Cited by 163 · see citing works
In Defense of Pharmacoepidemiology — Embracing the Yin and Yang of Drug Research
Avorn, J. (2007) · New England Journal of Medicine · doi:10.1056/nejmp0706892 · Cited by 162 · see citing works
Who is the marginal patient? Understanding instrumental variables estimates of treatment effects.
Harris, K. M., & Remler, D. K. (1998) · PubMed · Cited by 159 · see citing works
Drug Copayment and Adherence in Chronic Heart Failure: Effect on Cost and Outcomes
Cole, J. A., Norman, H. S., Weatherby, L. B., & Walker, A. M. (2006) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1592/phco.26.8.1157 · Cited by 130 · see citing works
Rassen, J. A., Brookhart, M. A., Glynn, R. J., Mittleman, M. A., & Schneeweiß, S. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2008.12.006 · Cited by 126 · see citing works
Self-citationSelective prescribing led to overestimation of the benefits of lipid-lowering drugs
Glynn, R. J., Schneeweiß, S., WANG, P., LEVIN, R., & Avorn, J. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.12.012 · Cited by 118 · see citing works
Self-citationSchneeweiß, S., Solomon, D. H., Wang, P. S., Rassen, J. A., & Brookhart, M. A. (2006) · Arthritis & Rheumatism · doi:10.1002/art.22219 · Cited by 92 · see citing works
Self-citationSolomon, D. H., Schneeweiß, S., Glynn, R. J., et al. (2003) · The American Journal of Medicine · doi:10.1016/j.amjmed.2003.08.025 · Cited by 84 · see citing works
Self-citationBrooks, J. M., Chrischilles, E. A., Scott, S. D., & Chen-Hardee, S. (2003) · Health Services Research · doi:10.1111/j.1475-6773.2003.00184.x · Cited by 81 · see citing works
Schneeweiß, S., Glynn, R. J., Avorn, J., & Solomon, D. H. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.06.002 · Cited by 74 · see citing works
Self-citationCombining ecological and individual variables to reduce confounding by indication:
Johnston, S. C. (2000) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00251-1 · Cited by 72 · see citing works
Evaluating the Validity of an Instrumental Variable Study of Neuroleptics
Brookhart, M. A., Rassen, J. A., Wang, P. S., Dormuth, C. R., Mogun, H., & Schneeweiß, S. (2007) · Medical Care · doi:10.1097/mlr.0b013e318070c057 · Cited by 69 · see citing works
Self-citationConjugated equine estrogens and peripheral arterial disease risk: The Women's Health Initiative
Hsia, J. A., Criqui, M. H., Herrington, D. M., Manson, J. E., Wu, L., Heckbert, S. R., Allison, M., McDermott, M. M., Robinson, J. G., & Masaki, K. (2006) · American Heart Journal · doi:10.1016/j.ahj.2005.09.005 · Cited by 65 · see citing works
Uses of Ecologic Studies in the Assessment of Intended Treatment Effects
Wen, S. W. (1999) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(98)00136-x · Cited by 51 · see citing works
Brooks, J. M., & Chrischilles, E. A. (2007) · Medical Care · doi:10.1097/mlr.0b013e318070c069 · Cited by 43 · see citing works
(2008) · Springer eBooks · doi:10.1007/978-1-4020-5614-7_510 · Cited by 11 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [34]Greene WH Econometric analysis 2003 Prentice Hall Upper Saddle River, NJ 5th edition
Cited by
Sorted by citations
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 · see citing works
Dekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., & Egger, M. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002742 · Cited by 525 · see citing works
Assumptions of IV Methods for Observational Epidemiology.
Didelez, V., Meng, S., & Sheehan, N. A. (2010) · Statistical Science · doi:10.1214/09-sts316 · Cited by 214 · see citing works
An introduction to instrumental variable assumptions, validation and estimation
Lousdal, M. L. (2018) · Emerging Themes in Epidemiology · doi:10.1186/s12982-018-0069-7 · Cited by 173 · see citing works
Thomas, K. H., Martin, R. M., Davies, N. M., Metcalfe, C., Windmeijer, F., & Gunnell, D. (2013) · BMJ · doi:10.1136/bmj.f5704 · Cited by 119 · see citing works
Kern, D., Auchincloss, A., Stehr, M., Diez Roux, A., Moore, L., Kanter, G., & Robinson, L. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14111394 · Cited by 58 · see citing works
Davies, N. M., Thomas, K. H., Taylor, A. E., Taylor, G. M., Martin, R. M., Munafò, M. R., & Windmeijer, F. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx014 · Cited by 55 · see citing works
Genser, B., Teles, C. A., Barreto, M. L., & Fischer, J. E. (2015) · Environmental Health · doi:10.1186/s12940-015-0047-2 · Cited by 23 · see citing works
The challenge of dementia prevention trials and the role of quasi-experimental studies
Lindhout, J. E., van Dalen, J. W., van Gool, W. A., & Richard, E. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13029 · Cited by 6 · see citing works
Sakellariou, C. (2023) · Child Indicators Research · doi:10.1007/s12187-023-10029-x · Cited by 4 · see citing works
- Authors
- Rassen, J. A., Brookhart, M. A., Glynn, R. J., Mittleman, M. A., & Schneeweiss, S.
- Affiliations
- Brigham and Women's Hospital (US)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Rassen, Jeremy A.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2009-12
- Volume
- 62
- Issue
- 12
- Pages
- 1226-1232
- ISSN
- 0895-4356 (ISSN-L)
- PMC
- PMC2905668
- OpenAlex
- W2066182646
- Cited by
- 164
- References
- 39
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 5 · 2022: 15 · 2021: 9 · 2020: 8 · 2019: 15 · 2018: 7 · 2017: 15 · 2016: 6 · 2015: 13 · 2014: 10 · 2013: 17 · 2012: 10
- FWCI
- 10.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Agency for Healthcare Research and Quality (2-RO1-HS10881, R01-HS010881)
- National Institute on Aging (R01AG021950)
- National Institute of Mental Health (U01MH078708)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- instrumental variables, causal inference, non-experimental data, randomized controlled trials, exchangeability, confounding, instruments, causal effect estimation, clinical epidemiology
- MeSH terms
- Humans, Patient Compliance, Practice Patterns, Physicians', Epidemiologic Research Design, Causality, Confounding Factors, Epidemiologic
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
