ArCo: An R package to Estimate Artificial Counterfactuals

Yuri R. Fonseca; Ricardo P. Masini; Marcelo C. Medeiros; Gabriel F.R. Vasconcelos

doi:10.32614/rj-2018-016

ArCo: An R package to Estimate Artificial Counterfactuals

Yuri R. Fonseca, Ricardo P. Masini, Marcelo C. Medeiros, Gabriel F.R. Vasconcelos

Center for Statistics & Machine Learning

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English (US)
Pages (from-to)	91-108
Number of pages	18
Journal	R Journal
Volume	10
Issue number	1
DOIs	https://doi.org/10.32614/rj-2018-016
State	Published - Jul 1 2018

All Science Journal Classification (ASJC) codes

Statistics and Probability
Numerical Analysis
Statistics, Probability and Uncertainty

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10.32614/rj-2018-016

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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.",

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AB - 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.

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ArCo: An R package to Estimate Artificial Counterfactuals

Abstract

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