The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions . In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. However, none of these make 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  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 new price policy on supermarket sales.
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. However, none of these make 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.
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potential outcomes approach to causal inference, most commonly known as the
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,
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a treatment (or "intervention ")
an intervention
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, to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions
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first formalize the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention; we then
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new price policy
permanent price reduction