Counterfactual Identification and Latent Space Enumeration in Discrete Outcome Models.
This article provides a unified framework for studying the identification of counterfactual parameters in a general class of discrete outcome models, allowing for endogenous regressors and multidimensional latent variables, all without parametric distributional assumptions. Our main theoretical resu...
| Publicado en: | Review of Economic Studies Vol. 93; no. 3; pp. 1847 - 1889 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193663107&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193663107 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: May2026 vid: 93 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 193663107 10.1093/restud/rdaf058 ppf: 1847 ppct: 42 formats: tig: atl: Counterfactual Identification and Latent Space Enumeration in Discrete Outcome Models. aug: au: Gu, Jiaying Russell, Thomas M Stringham, Thomas affil: Department of Economics, University of Toronto, Canada Department of Economics, Carleton University, Canada StataCorp, USA su: Latent variables Discrete choice models Algorithms Linear programming Nonparametric statistics Counterfactuals (Logic) Hyperplanes sug: subj: Latent variables Discrete choice models Algorithms Linear programming Nonparametric statistics Counterfactuals (Logic) Hyperplanes ab: This article provides a unified framework for studying the identification of counterfactual parameters in a general class of discrete outcome models, allowing for endogenous regressors and multidimensional latent variables, all without parametric distributional assumptions. Our main theoretical result is that, when the covariates are discrete, the infinite-dimensional latent variable distribution can be replaced with a finite-dimensional version that is equivalent from an identification perspective. The finite-dimensional latent variable distribution is constructed in practice by enumerating regions of the latent variable space with a new and efficient cell enumeration algorithm for hyperplane arrangements. We then show that bounds on a certain class of counterfactual parameters can be computed by solving a sequence of linear programming problems, and show how the researcher can introduce additional assumptions as constraints in the linear programmes. Finally, we apply the method to a mobile phone choice example with heterogeneous choice sets, and to an airline entry game example. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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