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CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0002-9086-7403
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
2015 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 68, no 1, 1-20 p.Article in journal (Refereed) Published
Abstract [en]

We describe the R package CovSel, which reduces the dimension of the covariate vector for the purpose of estimating an average causal effect under the unconfoundedness assumption. Covariate selection algorithms developed in De Luna, Waernbaum, and Richardson (2011) are implemented using model-free backward elimination. We show how to use the package to select minimal sets of covariates. The package can be used with continuous and discrete covariates and the user can choose between marginal co-ordinate hypothesis tests and kernel-based smoothing as model-free dimension reduction techniques.

Place, publisher, year, edition, pages
2015. Vol. 68, no 1, 1-20 p.
Keyword [en]
causal inference, dimension reduction, dr, np, R
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-111952DOI: 10.18637/jss.v068.i01ISI: 000366013800001OAI: oai:DiVA.org:umu-111952DiVA: diva2:874330
Available from: 2015-11-26 Created: 2015-11-26 Last updated: 2017-12-01Bibliographically approved

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Häggström, JennyPersson, EmmaWaernbaum, Ingeborgde Luna, Xavier
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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Output format
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