The R Journal: article published in 2018, volume 10:1

ArCo: An R package to Estimate Artificial Counterfactuals
Yuri R. Fonseca, Ricardo P. Masini, Marcelo C. Medeiros and Gabriel F. R. Vasconcelos , The R Journal (2018) 10:1, pages 91-108.

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 KiB)
CRAN packages: ArCo, boot, glmnet, Synth
CRAN Task Views implied by cited CRAN packages: Survival, Econometrics, MachineLearning, Optimization, SocialSciences, TimeSeries

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}},
  doi = {10.32614/RJ-2018-016},
  url = {https://doi.org/10.32614/RJ-2018-016},
  pages = {91--108},
  volume = {10},
  number = {1}
}