About: Set identification

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In statistics and econometrics, set identification (or partial identification) extends the concept of identifiability (or "point identification") in statistical models to situations where the distribution of observable variables is not informative of the exact value of a parameter, but instead constrains the parameter to lie in a strict subset of the parameter space. Statistical models that are set identified arise in a variety of settings in economics, including game theory and the Rubin causal model.

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  • In statistics and econometrics, set identification (or partial identification) extends the concept of identifiability (or "point identification") in statistical models to situations where the distribution of observable variables is not informative of the exact value of a parameter, but instead constrains the parameter to lie in a strict subset of the parameter space. Statistical models that are set identified arise in a variety of settings in economics, including game theory and the Rubin causal model. Though the use of set identification dates to a 1934 article by Ragnar Frisch, the methods were significantly developed and promoted by Charles Manski starting in the 1990s. Manski developed a method of worst-case bounds for accounting for selection bias. Unlike methods that make additional statistical assumptions, such as Heckman correction, the worst-case bounds rely only on the data to generate a range of supported parameter values. (en)
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  • In statistics and econometrics, set identification (or partial identification) extends the concept of identifiability (or "point identification") in statistical models to situations where the distribution of observable variables is not informative of the exact value of a parameter, but instead constrains the parameter to lie in a strict subset of the parameter space. Statistical models that are set identified arise in a variety of settings in economics, including game theory and the Rubin causal model. (en)
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  • Set identification (en)
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