Arbeitspapier
High dimensional and inference methods on structural an treatment effects
The goal of many empirical papers in economics is to provide an estimate of the causal or structural effect of a change in a treatment or policy variable, such as a government intervention or a price, on another economically interesting variable, such as unemployment or amount of a product purchased. Applied economists attempting to estimate such structural effects face the problems that economically interesting quantities like government policies are rarely randomly assigned and that the available data are often high-dimensional. Failure to address either of these issues generally leads to incorrect inference about structural effects, so methodology that is appropriate for estimating and performing inference about these effects when treatment is not randomly assigned and there are many potential control variables provides a useful addition to the tools available to applied economists.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP59/13
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Belloni, Alexandre
Chernozhukov, Victor
Hansen, Christian
- 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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2013
- DOI
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doi:10.1920/wp.cem.2013.5913
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
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
- Belloni, Alexandre
- Chernozhukov, Victor
- Hansen, Christian
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2013
Other Objects (12)
Inference on treatment effects after selection amongst high-dimensional controls
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Inference for high-dimensional sparse econometric models
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on average treatment effects in aggregate panel data settings
Inference on causal and structural parameters using many moment inequalities
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Estimation of treatment effects with high-dimensional controls
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Uniform inference in high-dimensional gaussian graphical models
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions
Inference on treatment effects after selection amongst high-dimensional controls
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Inference for high-dimensional sparse econometric models
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on average treatment effects in aggregate panel data settings
Inference on causal and structural parameters using many moment inequalities
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Estimation of treatment effects with high-dimensional controls
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Uniform inference in high-dimensional gaussian graphical models
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions
Inference on treatment effects after selection amongst high-dimensional controls
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Inference for high-dimensional sparse econometric models
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on average treatment effects in aggregate panel data settings
Inference on causal and structural parameters using many moment inequalities
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Estimation of treatment effects with high-dimensional controls
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Uniform inference in high-dimensional gaussian graphical models
Posterior inference in curved exponential families under increasing dimensions