A national retail chain implemented a new employee training program in all its stores within a single state (the "treatment" state) starting in January 2024. The program was not implemented in any other state. A data scientist is tasked with estimating the causal effect of this program on monthly store revenue. They have access to monthly revenue data from 2022 to the present for all stores nationwide. The data scientist observes that the treatment state and a neighboring state had very similar revenue trends from 2022 to 2023. Which causal inference method is most appropriate for this analysis, given the available data and the nature of the intervention?
The correct answer is Difference-in-Differences (DiD). This quasi-experimental method is ideal for estimating the causal effect of an intervention when a randomized experiment is not feasible. The DiD model compares the change in the outcome (revenue) before and after the intervention for the treatment group (the state with the program) to the same change for a control group (the neighboring state). The scenario explicitly mentions that the two states had similar pre-intervention trends, which satisfies the critical 'parallel trends' assumption required for a valid DiD analysis.
A Randomized Controlled Trial (RCT) is incorrect because the intervention was not randomized; it was applied to an entire state. The data scientist cannot retroactively randomize the stores.
A Directed Acyclic Graph (DAG) is a tool used to map causal assumptions and identify confounders to help design a study, but it is not the estimation method itself. The primary method used to calculate the effect in this scenario would be DiD.
An Autoregressive Integrated Moving Average (ARIMA) model is primarily used for forecasting a single time series. While it can be adapted for intervention analysis on the treated group's data, it does not inherently leverage the control group to account for external time-varying factors, which is the main strength of the DiD approach.
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What are the key assumptions of the Difference-in-Differences (DiD) method?
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How does DiD compare to other causal inference methods like RCTs?
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Why isn't ARIMA appropriate for this analysis compared to DiD?