Identification of causal effects using instrumental variables.¶
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444-455.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Causality
- Economics has undergone a causal-inference revolution following Angrist, Imbens, and Rubin's formalizations of instrumental variables, difference-in-differences, and treatment effects, enabling causal claims from quasi-experimental data.
This sourceFormalizes how an instrument creating exogenous variation in treatment identifies the local average treatment effect (LATE) under monotonicity and exclusion.
Supported in partVerified against the publisher's abstract
The abstract backs only the instrumental-variables and treatment-effect part: it embeds the IV estimand in the Rubin Causal Model and identifies LATE for compliers; it says nothing of difference-in-differences or a 'revolution'.
“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.”
- Economics has undergone a causal-inference revolution following Angrist, Imbens, and Rubin's formalizations of instrumental variables, difference-in-differences, and treatment effects, enabling causal claims from quasi-experimental data.
- Experimental Design
- The instrument creates exogenous variation in treatment that can be leveraged for causal inference, as Angrist, Imbens, and Rubin (1996) formalize in their identification of local average treatment effects.
This sourceFormalizes how an instrument creating exogenous variation in treatment identifies the local average treatment effect (for compliers) under explicit assumptions within the Rubin causal model.
SupportedVerified against the publisher's abstract
Angrist, Imbens and Rubin show that "under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers", and that absent those assumptions it has "no interpretation as an average causal effect". That complier-specific identification, illustrated with a draft-lottery instrument, is the local average treatment effect the sentence credits to them. The work states the result in terms of compliers rather than using the phrase local average treatment effect, and puts the exogeneity condition as ignorable assignment rather than as an instrument creating exogenous variation.
“under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers”
- The instrument creates exogenous variation in treatment that can be leveraged for causal inference, as Angrist, Imbens, and Rubin (1996) formalize in their identification of local average treatment effects.
Verification¶
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Links previously used in the corpus¶
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