Model Selection for Treatment Choice: Penalized Welfare Maximization.
This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to allocate a binary treatment to members of a population, based on their observable characteristics. We model this problem as a...
| Publicado en: | Econometrica Vol. 89; no. 2; pp. 825 - 849 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Mar2021
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| 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=149411844&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 149411844 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Mar2021 vid: 89 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 149411844 10.3982/ECTA16437 ppf: 825 ppct: 24 formats: tig: atl: Model Selection for Treatment Choice: Penalized Welfare Maximization. aug: au: Mbakop, Eric Tabord‐Meehan, Max affil: Department of Economics, University of Calgary Department of Economics, University of Chicago su: Patient selection Statistical decision making Statistical learning Assignment problems (Programming) Statistical models sug: subj: Patient selection Statistical decision making Statistical learning Assignment problems (Programming) Statistical models keyword: minimax‐regret statistical learning Treatment choice minimax‐regret statistical learning Treatment choice ab: This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to allocate a binary treatment to members of a population, based on their observable characteristics. We model this problem as a statistical decision problem where the policy maker must choose a subset of the covariate space to assign to treatment, out of a class of potential subsets. We focus on settings in which the policy maker may want to select amongst a collection of constrained subset classes: examples include choosing the number of covariates over which to perform best‐subset selection, and model selection when approximating a complicated class via a sieve. We adapt and extend results from statistical learning to develop the Penalized Welfare Maximization (PWM) rule. We establish an oracle inequality for the regret of the PWM rule which shows that it is able to perform model selection over the collection of available classes. We then use this oracle inequality to derive relevant bounds on maximum regret for PWM. An important consequence of our results is that we are able to formalize model‐selection using a "holdout" procedure, where the policy maker would first estimate various policies using half of the data, and then select the policy which performs the best when evaluated on the other half of the data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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