2021/03/11 by Fiammetta Menchetti, Menchetti, Fiammetta, Fabrizio Cipollini +3 · 1 citation
Decision Sciences · Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #Efficiency Analysis Using DEA #FOS: Economics and business #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2103.06740
openalex publication_date 2021/03/11 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28
The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models, which are instead very common in the econometrics literature. In this paper, we propose a novel approach, C-ARIMA, to define and estimate the causal effect of an intervention in a time series setting under the RCM. We first formalize the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention; we then check the validity of the proposed method with an extensive simulation study, comparing its performance against a standard intervention analysis approach. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The CausalArima R package provides an implementation of our proposed approach.