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

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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  • Angrist et al. (1996) and Heckman and Vytlacil (2005) refined the analysis by considering a variety of causal effect measures, each applicable to a special segment of the population.

    “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)

  • The algebraic machinery of counterfactual notation can be powerful in refining assumptions, deriving consistent estimands, bounding probabilities of necessary and sufficient causation, and combining experimental and nonexperimental data.

    “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

  • CACE modeling is one analytic method for incomplete adoption.

    “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]

  • IV analysis uses an instrument related to the exposure but not the outcome 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.

    “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]

  • Instrumental variable estimation to obtain effect estimates imposes a fourth assumption, typically effect homogeneity or monotonicity.

    “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]

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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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