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.
- 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.
- 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.
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