Guido W. Imbens, Davide Viviano
arXiv 1 Dec 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2312.00955 · PDF · DOI · OpenAlex · Extracted main text
This paper studies inference on treatment effects in panel data settings with unobserved confounding. We model outcome variables through a factor model with random factors and loadings. Such factors and loadings may act as unobserved confounders: when the treatment is implemented depends on time-varying factors, and who receives the treatment depends on unit-level confounders. We study the identification of treatment effects and illustrate the presence of a trade-off between time and unit-level confounding. We provide asymptotic results for inference for several Synthetic Control estimators and show that different sources of randomness should be considered for inference, depending on the nature of confounding. We conclude with a comparison of Synthetic Control estimators with alternatives for factor models.
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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 | Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wa… (2021) Synthetic difference-in-differences self | 1.000 | 14 | 5 | 100% |
| 2 | Athey, S., M. Bayati, N. Doudchenko, G. Imbens, and K. Khosravi (2021) Matrix completion methods for causal panel data models | 1.000 | 6 | 3 | 100% |
| 3 | Abadie, A., A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 0.928 | 10 | 5 | 80% |
| 4 | Shen, D., P. Ding, J. Sekhon, and B. Yu (2022) A tale of two panel data regressions | 0.843 | 3 | 3 | 100% |
| 5 | Moon, H. R. and M. Weidner (2017) Dynamic linear panel regression models with interactive fixed effects | 0.737 | 3 | 2 | 100% |
| 6 | Hirshberg, D. A (2021) Least squares with error in variables | 0.644 | 3 | 2 | 67% |
| 7 | Bai, J (2009) Panel data models with interactive fixed effects | 0.644 | 2 | 2 | 100% |
| 8 | Bai, J. and S. Ng (2019) Rank regularized estimation of approximate factor models | 0.644 | 2 | 2 | 100% |
| 9 | Imbens, G., N. Kallus, and X. Mao (2021) Controlling for unmeasured confounding in panel data using minimal bridge functions: From two-way fixed effects to factor models self | 0.644 | 2 | 2 | 100% |
| 10 | Shi, X., W. Miao, M. Hu, and E. T. Tchetgen (2021) Theory for identification and inference with synthetic controls: a proximal causal inference framework | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 39 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Synthetic Parallel Trends | 0.928 | 4 | 3 |
| 2 | On Policy Evaluation With Aggregate Time-Series Instruments | 0.811 | 4 | 2 |
| 3 | Triply Robust Panel Estimators | 0.811 | 4 | 2 |
| 4 | 2206.01779 | 0.511 | 2 | 1 |
| 5 | Causal Models for Longitudinal and Panel Data: A Survey | 0.511 | 2 | 1 |
| 6 | Inference for Synthetic Controls via Refined Placebo Tests | 0.405 | 1 | 1 |
| 7 | Efficient Difference-in-Differences and Event Study Estimators | 0.405 | 1 | 1 |
| 8 | Debiasing and $t$-tests for synthetic control inference on average causal effects | 0.000 | 2 | 1 |