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Cited by 4,230Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferenceStatistical Methods in Clinical Trials

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

Angrist, Joshua D., Imbens, Guido W., Rubin, Donald B.·1996·Journal of the American Statistical Association
DOI 10.1080/01621459.1996.10476902
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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.

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Crossref · 10.1080/01621459.1996.10476902

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Research works printed but not linked: 13

  1. Angrist J. D., 1990, American Economic Review, 80, 313
  2. Cox D. R., 1958, Planning of Experiments
  3. De Tray D., 1982, American Economic Review, 72, 133
  4. Fisher R. A., 1918, Eugenics Review, 10, 213
  5. Heckman J., 1990, American Economic Review, 80, 313
  6. Holland P., 1988, Sociological Methodology, 449
  7. Imbens G. W., 1994, “On the Fragility of Instrumental Variables Estimators,”
  8. Lalonde R., 1986, American Economic Review, 76, 604
  9. McClellan M., 1994, “Marginal Benefits of Medical Treatment Intensity: Acute Myocardial Infarction in the Elderly,”
  10. Robins J. M., 1989, Health Service Research Methodology: A Focus on AIDS
  11. 10.1080/01621459.1980.10477517 · DOI · Paper page
  12. Schultz H., 1928, Statistical Laws of Supply and Demand
  13. Wright S., 1928, Appendix to The Tariff on Animal and Vegetable Oils

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Metadata

Authors
Angrist, J. D., Imbens, G. W., & Rubin, D. B.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Harvard University (US)
ORCID
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
DOI
10.1080/01621459.1996.10476902
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

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