The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Identification of Causal Effects Using Instrumental Variables
Abstract
We outline a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable. To address the problems associated with comparing subjects by the ignorable assignment—an “intention-to-treat analysis”—we make use of instrumental variables, which have long been used by economists in the context of regression models with constant treatment effects. We show that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers. Without these assumptions, the IV estimand is simply the ratio of intention-to-treat causal estimands with no interpretation as an average causal effect. The advantages of embedding the IV approach in the RCM are that it clarifies the nature of critical assumptions needed for a causal interpretation, and moreover allows us to consider sensitivity of the results to deviations from key assumptions in a straightforward manner. We apply our analysis to estimate the effect of veteran status in the Vietnam era on mortality, using the lottery number that assigned priority for the draft as an instrument, and we use our results to investigate the sensitivity of the conclusions to critical assumptions.
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
“Angrist et al. (1996) and Heckman and Vytlacil (2005) further refined this analysis by considering a variety of causal effect measures, each applicable to a special (albeit non-identifiable and transient) segment of the population.”
From Causal inference in statistics: An overview (2009) · cites this paper as Angrist et al. (1996)
In the citing paper
“the algebraic machinery offered by the counterfactual notation, Y x (u), once a problem is properly formalized, can be extremely powerful in refining assumptions (Angrist et al., 1996, Heckman andVytlacil, 2005), deriving consistent estimands (Robins, 1986), bounding probabilities of necessary and sufficient causation (Tian and Pearl, 2000), and combining data from experimental and nonexperimental”
From An Introduction to Causal Inference (2010) · cites this paper as Angrist et al., 1996
In the citing paper
“One analytical approach to accounting for incomplete adoption is to use Complier Average Causal Effect (commonly known as CACE) modeling (3, 9, 60).”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [3]
In the citing paper
“IV analysis was first proposed a century ago and is an approach to causal inference that uses an IV, or “instrument” — which is related to the exposure but not the outcome of interest other than through its association with the exposure — to make causal effect estimates in the presence of unobserved confounding of the exposure and the outcome.”
From Mendelian randomization (2022) · cites this paper as [2]
In the citing paper
“When instrumental variable estimation is used to obtain effect estimates, a fourth assumption should then be stated: typically, an assumption of effect homogeneity53 or monotonicity.54”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [54]
In the citing paper
“If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation.”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [6]
Citations
Cites
Limited-Dependent and Qualitative Variables in Econometrics
Maddala, G. S. (1983) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810176 · Cited by 13,268
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
Self-citation(2013) · American Economic Review · doi:10.1257/aer.103.6.i · Cited by 7,413
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Identification and estimation of local average treatment effects
Imbens, G. W., & Angrist, J. D. (1994) · Econometrica · doi:10.2307/2951620 · Cited by 4,101
Self-citationDoes Compulsory School Attendance Affect Schooling and Earnings?
Angrist, J. D., & Krueger, A. B. (1991) · The Quarterly Journal of Economics · doi:10.2307/2937954 · Cited by 2,565
Self-citationBayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Self-citationThe Method of Path Coefficients
Wright, S. (1934) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732676 · Cited by 2,388
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Neyman, J., Dabrowska, D. M., & Speed, T. P. (1990) · Statistical Science · doi:10.1214/ss/1177012031 · Cited by 1,680
Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment
Rubin, D. B. (1980) · Journal of the American Statistical Association · doi:10.2307/2287653 · Cited by 1,368
Self-citation
Research works printed but not linked: 13
- Angrist J. D., 1990, American Economic Review, 80, 313
- Cox D. R., 1958, Planning of Experiments
- De Tray D., 1982, American Economic Review, 72, 133
- Fisher R. A., 1918, Eugenics Review, 10, 213
- Heckman J., 1990, American Economic Review, 80, 313
- Holland P., 1988, Sociological Methodology, 449
- Imbens G. W., 1994, “On the Fragility of Instrumental Variables Estimators,”
- Lalonde R., 1986, American Economic Review, 76, 604
- McClellan M., 1994, “Marginal Benefits of Medical Treatment Intensity: Acute Myocardial Infarction in the Elderly,”
- Robins J. M., 1989, Health Service Research Methodology: A Focus on AIDS
- 10.1080/01621459.1980.10477517 · DOI · Paper page
- Schultz H., 1928, Statistical Laws of Supply and Demand
- Wright S., 1928, Appendix to The Tariff on Animal and Vegetable Oils
Cited by
Sorted by citations
Items 1,001–1,064 of 1,064
Goldberg, S., Wyss, D., & Bächtiger, A. (2019) · Political Studies · doi:10.1177/0032321719843967 · Cited by 55
Who Gets a Second Chance? Effectiveness and Equity in Supervision of Criminal Offenders
Rose, E. K. (2020) · The Quarterly Journal of Economics · doi:10.1093/qje/qjaa046 · Cited by 47
Smart Matching Platforms and Heterogeneous Beliefs in Centralized School Choice
Arteaga, F., Kapor, A., Neilson, C., & Zimmerman, S. (2022) · The Quarterly Journal of Economics · doi:10.1093/qje/qjac013 · Cited by 47
Causal logistic models for non‐compliance under randomized treatment with univariate binary response
Have, T. R. T., Joffe, M. M., & Cary, M. S. (2003) · Statistics in Medicine · doi:10.1002/sim.1401 · Cited by 47
The Causal Impact of Removing Children from Abusive and Neglectful Homes
Bald, A., Chyn, E. T., Hastings, J., & Machelett, M. (2019) · National Bureau of Economic Research · doi:10.3386/w25419 · Cited by 60
Ben‐David, I., & Bos, M. (2020) · Review of Financial Studies · doi:10.1093/rfs/hhaa094 · Cited by 46
Increasing the Electoral Participation of Immigrants: Experimental Evidence from France
Pons, V., & Liegey, G. (2018) · The Economic Journal · doi:10.1111/ecoj.12584 · Cited by 46
The causal effect of education on HIV stigma in Uganda: Evidence from a natural experiment
Tsai, A. C., & Venkataramani, A. S. (2015) · Social Science & Medicine · doi:10.1016/j.socscimed.2015.08.009 · Cited by 44
Incapacitation: Revisiting an Old Question with a New Method and New Data
Sweeten, G., & Apel, R. J. (2007) · Journal of Quantitative Criminology · doi:10.1007/s10940-007-9032-4 · Cited by 46
Instrumental variables estimates of peer effects in social networks
An, W. (2014) · Social Science Research · doi:10.1016/j.ssresearch.2014.08.011 · Cited by 46
Handling Missing Data in Randomized Experiments with Noncompliance
Jo, B., Ginexi, E. M., & Ialongo, N. S. (2010) · Prevention Science · doi:10.1007/s11121-010-0175-4 · Cited by 46
Statistical Analysis for Multisite Trials Using Instrumental Variables With Random Coefficients
Raudenbush, S. W., Reardon, S. F., & Nomi, T. (2012) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2012.689610 · Cited by 46
Kim, J. S., Capotosto, L., Hartry, A., & Fitzgerald, R. (2011) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373711399148 · Cited by 55
McNamee, R. (2009) · Statistics in Medicine · doi:10.1002/sim.3636 · Cited by 46
Identification and estimation of causal effects with outcomes truncated by death
Wang, L., Zhou, X., & Richardson, T. S. (2017) · Biometrika · doi:10.1093/biomet/asx034 · Cited by 46
On Heckits, LATE, and Numerical Equivalence
Kline, P., & Walters, C. (2018) · Econometrica · doi:10.3982/ecta15444 · Cited by 45
Kirchweger, S., & Kantelhardt, J. (2015) · Land Use Policy · doi:10.1016/j.landusepol.2015.05.005 · Cited by 50
Randomized Control Trials in the Field of Development
Bédécarrats, F., Guérin, I., & Roubaud, F. (2020) · doi:10.1093/oso/9780198865360.001.0001 · Cited by 53
Testing Attrition Bias in Field Experiments
Ghanem, D., Hirshleifer, S., & Ortiz‐Becerra, K. (2023) · The Journal of Human Resources · doi:10.3368/jhr.0920-11190r2 · Cited by 58
Therapeutic non‐adherence: a rational behavior revealing patient preferences?
Lamiraud, K., & Geoffard, P. (2007) · Health Economics · doi:10.1002/hec.1214 · Cited by 46
Kahn, K. L., Tisnado, D. M., Adams, J., Liu, H., Chen, W., Hu, F. A., Mangione, C. M., Hays, R. D., & Damberg, C. L. (2006) · Health Services Research · doi:10.1111/j.1475-6773.2006.00604.x · Cited by 46
A Broader Template for Analyzing Broken Randomized Experiments
BARNARD, J., DU, J., Hill, J., & Rubin, D. B. (1998) · Sociological Methods & Research · doi:10.1177/0049124198027002005 · Cited by 46
Self-citationBang, H., & Davis, C. E. (2007) · Statistics in Medicine · doi:10.1002/sim.2663 · Cited by 35
Jo, B., & Muthén, B. (2001) · Psychology Press eBooks · doi:10.4324/9781410601858-8 · Cited by 46
Heckman, J. J. (2008) · National Bureau of Economic Research · doi:10.3386/w13934 · Cited by 46
Elections as instruments for punishing bad representatives and selecting good ones
Crisp, B. F., Olivella, S., Potter, J. D., & Mishler, W. (2013) · Electoral Studies · doi:10.1016/j.electstud.2013.08.017 · Cited by 45
Enhancing energy efficiency of Indian industries: Effectiveness of PAT scheme
Oak, H., & Bansal, S. (2022) · Energy Economics · doi:10.1016/j.eneco.2022.106220 · Cited by 45
Causal inference in drug discovery and development
Michoel, T., & Zhang, J. D. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103737 · Cited by 49
Epple, D. N., Romano, R. M., & Zimmer, R. (2016) · Handbook of the economics of education · doi:10.1016/b978-0-444-63459-7.00003-8 · Cited by 48
Bank privatization, finance, and growth
Berkowitz, D., Hoekstra, M., & Schoors, K. J. L. (2014) · Journal of Development Economics · doi:10.1016/j.jdeveco.2014.05.005 · Cited by 47
Dissonant Conclusions When Testing the Validity of an Instrumental Variable
Yang, F., Zubizarreta, J. R., Small, D. S., Lorch, S. A., & Rosenbaum, P. R. (2014) · The American Statistician · doi:10.1080/00031305.2014.962764 · Cited by 45
Under What Assumptions Do Site-by-Treatment Instruments Identify Average Causal Effects?
Reardon, S. F., & Raudenbush, S. W. (2013) · Sociological Methods & Research · doi:10.1177/0049124113494575 · Cited by 45
The Impact of Oportunidades on Consumption, Savings and Transfers*
Angelucci, M., Attanasio, O. P., & Maro, V. D. (2012) · Fiscal Studies · doi:10.1111/j.1475-5890.2012.00163.x · Cited by 45
Hyde, S. (2017) · Cornell University Press eBooks · doi:10.7591/9780801460777 · Cited by 45
Instrumental Variable Estimation with a Stochastic Monotonicity Assumption
Small, D. S., Tan, Z., Ramsahai, R. R., Lorch, S. A., & Brookhart, M. A. (2017) · Statistical Science · doi:10.1214/17-sts623 · Cited by 45
Weather index insurance, agricultural input use, and crop productivity in Kenya
Sibiko, K. W., & Qaim, M. (2019) · Food Security · doi:10.1007/s12571-019-00987-y · Cited by 45
Alternative Assumptions to Identify LATE in Fuzzy Regression Discontinuity Designs
Dong, Y. (2018) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.12249 · Cited by 45
Randell, E., McNamara, R. L., Delport, S., Busse, M. E., Hastings, R. P., Gillespie, D., Williams‐Thomas, R., Brookes‐Howell, L., Romeo, R., Boadu, J., et al. (2019) · Trials · doi:10.1186/s13063-019-3205-y · Cited by 57
How Do Mortgage Refinances Affect Debt, Default, and Spending? Evidence from HARP
Abel, J., & Fuster, A. (2021) · American Economic Journal Macroeconomics · doi:10.1257/mac.20180116 · Cited by 45
The effects of housing and neighborhood conditions on child mortality
Jacob, B. A., Ludwig, J., & Miller, D. L. (2012) · Journal of Health Economics · doi:10.1016/j.jhealeco.2012.10.008 · Cited by 44
Severity of bias of a simple estimator of the causal odds ratio in Mendelian randomization studies
Harbord, R. M., Didelez, V., Palmer, T., Meng, S., Sterne, J. A. C., & Sheehan, N. A. (2012) · Statistics in Medicine · doi:10.1002/sim.5659 · Cited by 46
Using Cluster Analysis in Program Evaluation
Peck, L. R. (2005) · Evaluation Review · doi:10.1177/0193841x04266335 · Cited by 45
Powell, J., Williams, V., Atherton, H., Bennett, K., Yang, Y., Davoudianfar, M., Hellsing, A., Martin, A., Mollison, J., Shanyinde, M., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/16804 · Cited by 44
An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score
Hu, W., Chan, C. W., Zubizarreta, J. R., & Escobar, G. J. (2018) · Manufacturing & Service Operations Management · doi:10.1287/msom.2017.0658 · Cited by 44
Watkins, E., Newbold, A., Tester-Jones, M., Collins, L. M., & Mostazir, M. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.1937 · Cited by 40
Assessing ‘what works’ in international development: meta-analysis for sophisticated dummies
Duvendack, M., Hombrados, J. G., Palmer-Jones, R., & Waddington, H. (2012) · Journal of Development Effectiveness · doi:10.1080/19439342.2012.710642 · Cited by 52
Urban upgrading in Rio de Janeiro: Evidence from the Favela-Bairro programme
Atuesta, L. H., & Soares, Y. (2018) · Urban Studies · doi:10.1177/0042098016669290 · Cited by 38
The fly connectome reveals a path to the effectome
Pospisil, D. A., Aragon, M. J., Dorkenwald, S., Matsliah, A., Sterling, A. R., Schlegel, P., Yu, S.-C., McKellar, C. E., Costa, M., Eichler, K., et al. (2024) · Nature · doi:10.1038/s41586-024-07982-0 · Cited by 36
Not Too Late: Improving Academic Outcomes Among Adolescents
Guryan, J., Ludwig, J., Bhatt, M., Cook, P., Davis, J. M. V., Dodge, K., Farkas, G., Fryer, R., Mayer, S., Pollack, H., et al. (2021) · National Bureau of Economic Research · doi:10.3386/w28531 · Cited by 25
Wells, K. B., Tang, L., Carlson, G. A., & Asarnow, J. R. (2012) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2011.0074 · Cited by 28
Survey research methods in evaluation and case-control studies
Kalton, G., & Piesse, A. (2007) · Statistics in Medicine · doi:10.1002/sim.2796 · Cited by 27
Nagengast, B., Brisson, B. M., Hulleman, C. S., Gaspard, H., Häfner, I., & Trautwein, U. (2018) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1289359 · Cited by 24
Schaub, M. P., Tiburcio, M., Martinez, N., Ambekar, A., Balhara, Y. P. S., Wenger, A., Monezi Andrade, A. L., Padruchny, D., Osipchik, S., Gehring, E., et al. (2018) · Addiction · doi:10.1111/add.14034 · Cited by 25
Challenges and Solutions to Pre- and Post-Randomization Subgroup Analyses
Desai, M., Pieper, K. S., & Mahaffey, K. (2014) · Current Cardiology Reports · doi:10.1007/s11886-014-0531-2 · Cited by 26
Norrie, J., Davidson, K., Tata, P., & Gumley, A. (2013) · Psychology and Psychotherapy: Theory, Research and Practice · doi:10.1111/papt.12004 · Cited by 23
Nelson, L. A., Spieker, A. J., Mayberry, L. S., McNaughton, C., & Greevy, R. A. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab254 · Cited by 23
Achievement outcomes among high school graduates in college and career readiness programs of study
Castellano, M., Ewart Sundell, K., & Richardson, G. B. (2017) · Peabody Journal of Education · doi:10.1080/0161956x.2017.1302220 · Cited by 19
Fotso, J. C., Bellhouse, L., Vesel, L., & Jezman, Z. (2015) · African Population Studies · doi:10.11564/29-1-717 · Cited by 16
Can Restorative Justice Conferencing Reduce Recidivism? Evidence From the Make-it-Right Program
Shem-Tov, Y., Raphael, S., & Skog, A. (2021) · National Bureau of Economic Research · doi:10.3386/w29150 · Cited by 15
New Perspectives on Causal Mediation Analysis
Wang, X., & Sobel, M. E. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6094-3_12 · Cited by 14
Pedula, K. L., Hillier, T. A., Ogasawara, K. K., Vesco, K. K., Lubarsky, S., Oshiro, C. E. S., & VanMarter, J. (2019) · Contemporary Clinical Trials · doi:10.1016/j.cct.2019.105829 · Cited by 7
Fütterer, T., Bardach, L., Kuhn, J., Keller, S. D., & Gerjets, P. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10133-8 · Cited by 17
Combs, A., Tierney, G., Guay, B., Merhout, F., Bail, C. A., Hillygus, D. S., & Volfovsky, A. (2022) · doi:10.31235/osf.io/cwgu5 · Cited by 5
A Topological Perspective on Causal Inference
Ibeling, D., & Icard, T. (2021) · arXiv · doi:10.48550/arxiv.2107.08558 · Cited by 4
Metadata
- Authors
- Angrist, J. D., Imbens, G. W., & Rubin, D. B.
- Affiliations
- Massachusetts Institute of Technology (US)
- Harvard University (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1996-06
- Volume
- 91
- Issue
- 434
- Pages
- 444-455
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W3124658416
- Cited by
- 4,230
- References
- 46
- Citations by year
- 2026: 207 · 2025: 293 · 2024: 268 · 2023: 306 · 2022: 309 · 2021: 248 · 2020: 298 · 2019: 218 · 2018: 214 · 2017: 196 · 2016: 222 · 2015: 168 · 2014: 171 · 2013: 142 · 2012: 154
- FWCI
- 16.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- instrumental variables, causal inference, non-compliance, compliers, intention-to-treat analysis, potential outcomes, binary treatment, average causal effect, sensitivity analysis, draft lottery, veteran status, Vietnam War, mortality
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
