The R Journal: accepted article

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ArCo: An R package to Estimate Artificial Counterfactuals PDF download
Yuri R. Fonseca, Ricardo P. Masini, Marcelo C. Medeiros and Gabriel F. R. Vasconcelos

Abstract In this paper we introduce the ArCo package for R which consists of a set of functions to implement the the Artificial Counterfactual (ArCo) methodology to estimate causal effects of an intervention (treatment) on aggregated data and when a control group is not necessarily available. The ArCo method is a two-step procedure, where in the first stage a counterfactual is estimated from a large panel of time series from a pool of untreated peers. In the second-stage, the average treatment effect over the post-intervention sample is computed. Standard inferential procedures are available. The package is illustrated with both simulated and real datasets.

Received: 2017-05-09; online 2018-05-16, supplementary material, (2.9 Kb)
CRAN packages: ArCo, boot, glmnet, Synth, Synth, Synth, Synth
CRAN Task Views implied by cited CRAN packages: Survival, Econometrics, MachineLearning, Optimization, SocialSciences, TimeSeries


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This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-016,
  author = {Yuri R. Fonseca and Ricardo P. Masini and Marcelo C.
          Medeiros and Gabriel F. R. Vasconcelos},
  title = {{ArCo: An R package to Estimate Artificial Counterfactuals}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-016/index.html}
}