ArCo: An R package to Estimate Artificial Counterfactuals

Authors

• Yuri R. Fonseca, Military Institute of Engineering
• Ricardo P. Masini, São Paulo School of Economics
• Marcelo C. Medeiros, Pontifical Catholic University of Rio de Janeiro
• Gabriel F.R. Vasconcelos, Pontifical Catholic University of Rio de Janeiro

Date of this Version

7-2018

Document Type

Article

Citation

The R Journal (July 2018) 10(1); Editor: John Verzani

Comments

Copyright 2018, The R Foundation. Open access material. License: CC BY 4.0 International

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.