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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-016.zip
Survival, Econometrics, MachineLearning, Optimization, SocialSciences, TimeSeries
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For attribution, please cite this work as
Fonseca, et al., "ArCo: An R package to Estimate Artificial Counterfactuals", The R Journal, 2018
BibTeX citation
@article{RJ-2018-016,
author = {Fonseca, Yuri R. and Masini, Ricardo P. and Medeiros, Marcelo C. and Vasconcelos, Gabriel F. R.},
title = {ArCo: An R package to Estimate Artificial Counterfactuals},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-016},
doi = {10.32614/RJ-2018-016},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {91-108}
}