Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
arXiv 11 Mar 2021 · Econometrics · 3 citations (OpenAlex)
arXiv:2103.06740 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bojinov, I. and Shephard, N (2019) Time series experiments and causal estimands: exact randomization tests and trading | 1.000 | 7 | 3 | 100% |
| 2 | Bojinov, I., Rambachan, A., and Shephard, N (2020) Panel experiments and dynamic causal effects: A finite population perspective | 0.928 | 4 | 3 | 100% |
| 3 | Box, G. E. and Tiao, G. C (1976) Comparison of forecast and actuality | 0.874 | 5 | 2 | 100% |
| 4 | Callaway, B. and Sant’Anna, P. H (2020) Difference-in-differences with multiple time periods | 0.874 | 5 | 2 | 100% |
| 5 | Box, G. E. and Tiao, G. C (1975) Intervention analysis with applications to economic and environmental problems | 0.843 | 3 | 3 | 100% |
| 6 | Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., and Scott, S… (2015) Inferring causal impact using Bayesian structural time-series models | 0.811 | 4 | 2 | 100% |
| 7 | Papadogeorgou, G., Mealli, F., Zigler, C. M., Dominici, F., Wasfy, J… (2018) Causal impact of the hospital readmissions reduction program on hospital readmissions and mortality self | 0.737 | 3 | 2 | 100% |
| 8 | Sun, L. and Abraham, S (2020) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 9 | Bhattacharyya, M. and Layton, A. P (1979) Effectiveness of seat belt legislation on the queensland road toll—an Australian case study in intervention analysis | 0.644 | 2 | 2 | 100% |
| 10 | Imbens, G. W. and Rubin, D. B (2015) Causal inference in Statistics, Social, and Biomedical Sciences | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Experiments | 0.737 | 3 | 2 |
| 2 | Dynamic causal inference with time series data | 0.405 | 1 | 1 |