Show simple item record

dc.contributor.authorBelloni, Alberto
dc.contributor.authorChernozhukov, Victor V
dc.contributor.authorFernandez-Val, Ivan
dc.contributor.authorHansen, Christian B.
dc.date.accessioned2018-02-20T20:13:24Z
dc.date.available2018-02-20T20:13:24Z
dc.date.issued2017-01
dc.identifier.issn0012-9682
dc.identifier.issn1468-0262
dc.identifier.urihttp://hdl.handle.net/1721.1/113842
dc.description.abstractIn 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, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high-dimensional components of the model. These include a number of supporting auxiliary 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.en_US
dc.publisherThe Econometric Societyen_US
dc.relation.isversionofhttp://dx.doi.org/10.3982/ECTA12723en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleProgram Evaluation and Causal Inference With High-Dimensional Dataen_US
dc.typeArticleen_US
dc.identifier.citationBelloni, A. et al. “Program Evaluation and Causal Inference With High-Dimensional Data.” Econometrica 85, 1 (2017): 233–298en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Economicsen_US
dc.contributor.mitauthorBelloni, Alberto
dc.contributor.mitauthorChernozhukov, Victor V
dc.contributor.mitauthorFernandez-Val, Ivan
dc.contributor.mitauthorHansen, Christian B.
dc.relation.journalEconometricaen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-02-20T17:29:18Z
dspace.orderedauthorsBelloni, A.; Chernozhukov, V.; Fernandez-Val, I.; Hansen, C.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-3250-6714
mit.licenseOPEN_ACCESS_POLICYen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record