CoChat
Sign Up
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
Open this paper in CoChat — free account

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

Crossref · 10.1080/01621459.1996.10476902

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

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

Cited by

Sorted by citations

See all 4,230

Related

See all

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

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