CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects
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
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.
causal inference, dimension reduction, dr, np, R
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:umu:diva-111952DOI: 10.18637/jss.v068.i01ISI: 000366013800001OAI: oai:DiVA.org:umu-111952DiVA: diva2:874330