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dc.contributor.authorBelloni, A.en_US
dc.contributor.authorChernozhukov, V.en_US
dc.contributor.authorHansen, C.en_US
dc.date.accessioned2012-06-19en_US
dc.date.accessioned2012-10-16T13:08:23Z-
dc.date.available2012-10-16T13:08:23Z-
dc.date.issued2012en_US
dc.identifier.pidoi:10.1920/wp.cem.2012.1012en_US
dc.identifier.urihttp://hdl.handle.net/10419/64668-
dc.description.abstractWe propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances where the number of controls may be much 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 conditioning on a relatively small number of controls whose identities are unknown. The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of controls. We develop a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the post-double-selection method. Our results apply to Lasso-type methods used for covariate selection as well as to any other model selection method that is able to find a sparse model with good approximation properties. 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 illustrate the use of the developed methods with numerical simulations and an application to the effect of abortion on crime rates.en_US
dc.language.isoengen_US
dc.publisherCentre for Microdata Methods and Practice Londonen_US
dc.relation.ispartofseriescemmap working paper CWP10/12en_US
dc.subject.ddc330en_US
dc.subject.keywordtreatment effectsen_US
dc.subject.keywordpartially linear modelen_US
dc.subject.keywordhigh-dimensional-sparse regressionen_US
dc.subject.keywordinference under imperfect model selectionen_US
dc.subject.keyworduniformly valid inference after model selectionen_US
dc.titleInference on treatment effects after selection amongst high-dimensional controlsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn717882810en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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