Inference on Treatment Effects after Selection among High-Dimensional Controls†.
We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make inform...
| Publicado en: | Review of Economic Studies Vol. 81; no. 2; pp. 608 - 651 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Apr2014
|
| Materias: | |
| 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=95756221&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 95756221 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: Apr2014 vid: 81 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 95756221 10.1093/restud/rdt044 ppf: 608 ppct: 43 formats: tig: atl: Inference on Treatment Effects after Selection among High-Dimensional Controls†. aug: au: Belloni, Alexandre Chernozhukov, Victor Hansen, Christian affil: Duke University MIT University of Chicago su: Confidence intervals Sample size (Statistics) Statistical hypothesis testing Statistical sampling Statistical tolerance regions sug: subj: Marketing Research and Public Opinion Polling Confidence intervals Sample size (Statistics) Statistical hypothesis testing Statistical sampling Statistical tolerance regions keyword: Average treatment effects High-dimensional-sparse regression Inference under imperfect model selection Lasso Orthogonality of estimating equations with respect to nuisance parameters Partially linear model Treatment effects Uniformly valid inference after model selection Average treatment effects High-dimensional-sparse regression Inference under imperfect model selection Lasso Orthogonality of estimating equations with respect to nuisance parameters Partially linear model Treatment effects Uniformly valid inference after model selection ab: We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small approximation error by including a relatively small number of variables whose identities are unknown. The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of regressors. We develop a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the “post-double-selection” method. The main attractive feature of our method is that it allows for imperfect selection of the controls and provides confidence intervals that are valid uniformly across a large class of models. In contrast, standard post-model selection estimators fail to provide uniform inference even in simple cases with a small, fixed number of controls. Thus, our method resolves the problem of uniform inference after model selection for a large, interesting class of models. We also present a generalization of our method to a fully heterogeneous model with a binary treatment variable. We illustrate the use of the developed methods with numerical simulations and an application that considers the effect of abortion on crime rates. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|