Arbeitspapier
Program evaluation and causal inference with high-dimensional data
In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment condition framework, which arises from structural equation models in econometrics. Here too the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model. These include a number of supporting auxilliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity-based estimation of regression functions for function-valued outcomes.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP13/16
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
-
Belloni, Alexandre
Chernozhukov, Victor
Fernández-Val, Iván
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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2016
- DOI
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doi:10.1920/wp.cem.2016.1316
- Handle
- Last update
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10.03.2025, 11:45 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
- Fernández-Val, Iván
- Hansen, Christian
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2016
Other Objects (12)
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Uniform inference in high-dimensional gaussian graphical models
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Inference on causal and structural parameters using many moment inequalities
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
L1-Penalised quantile regression in high-dimensional sparse models
Inference for high-dimensional sparse econometric models
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Uniform inference in high-dimensional gaussian graphical models
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Inference on causal and structural parameters using many moment inequalities
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
L1-Penalised quantile regression in high-dimensional sparse models
Inference for high-dimensional sparse econometric models
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Program evaluation with high-dimensional data
Uniform inference in high-dimensional gaussian graphical models
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Inference on causal and structural parameters using many moment inequalities
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)