CovSel : An R Package for Covariate Selection When Estimating Average Causal Effects

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 b...

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Bibliographic Details
Published in:Journal of statistical software 2015-11, Vol.68 (1), p.1-20
Main Authors: Häggström, Jenny, Persson, Emma, Waernbaum, Ingeborg, de Luna, Xavier
Format: Article
Language:English
Subjects:
causal inference
dimension reduction
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Summary: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.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v068.i01