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Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects | Häggström | Journal of Statistical Software
Authors: Jenny Häggström, Emma Persson, Ingeborg Waernbaum, Xavier de Luna
Title: CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects
Abstract: 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.

Page views:: 785. Submitted: 2013-10-09. Published: 2015-11-24.
Paper: CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects     Download PDF (Downloads: 754)
Supplements:
CovSel_1.2.1.tar.gz: R source package Download (Downloads: 50; 146KB)
v68i01.R: R replication code Download (Downloads: 57; 3KB)
cps3_controls.txt: Supplementary data Download (Downloads: 51; 67KB)
nswre74_treated.txt: Supplementary data Download (Downloads: 50; 29KB)

DOI: 10.18637/jss.v068.i01

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.