Arbeitspapier
Proxy variables and nonparametric identification of causal effects
Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 2016:12
- Classification
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Wirtschaft
Semiparametric and Nonparametric Methods: General
- Subject
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average treatment effect
observational studies
potential outcomes
unobserved confounders
- Event
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Geistige Schöpfung
- (who)
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DeLuna, Xavier
Fowler, Philip
Johansson, Per-Olov
- Event
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Veröffentlichung
- (who)
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Institute for Evaluation of Labour Market and Education Policy (IFAU)
- (where)
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Uppsala
- (when)
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2016
- Handle
- Last update
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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
- DeLuna, Xavier
- Fowler, Philip
- Johansson, Per-Olov
- Institute for Evaluation of Labour Market and Education Policy (IFAU)
Time of origin
- 2016
Other Objects (12)
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