Arbeitspapier
Identification of causal models with unobservables: A self-report approach
This paper presents a novel self-report approach to identify a general causal model with an unobserved covariate, which can be unobserved heterogeneity or an unobserved choice variable. It shows that a carefully designed noninvasive survey procedure can provide enough information to identify the complete causal model through the joint distribution of the observables and the unobservable. The global nonparametric point identification results provide sufficient conditions under which the joint distribution of four observables, two in a causal model and two from surveys, uniquely determines the joint distribution of the unobservable in the causal model and the four observables. The identification of such a joint distribution including the unobserved covariate implies that the complete causal model is identified.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP30/21
- Classification
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Wirtschaft
Econometrics
Semiparametric and Nonparametric Methods: General
- Subject
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Causal model
Measurement error model
Nonparametric identification
- Event
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Geistige Schöpfung
- (who)
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Hu, Yingyao
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2021
- DOI
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doi:10.47004/wp.cem.2021.3021
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Hu, Yingyao
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2021
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