Saeyoung Rho, Cyrus Illick, Samhitha Narasipura, Alberto Abadie, Daniel Hsu, Vishal Misra
arXiv 6 Jan 2026 · Machine Learning
arXiv:2601.03099 · PDF · DOI · OpenAlex · Extracted main text
The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing methods typically treat the ordering of pre-intervention time indices interchangeable. This invariance means they may not fully take advantage of temporal structure when strong trends are present. We propose Time-Aware Synthetic Control (TASC), which employs a state-space model with a constant trend while preserving a low-rank structure of the signal. TASC uses the Kalman filter and Rauch-Tung-Striebel smoother: it first fits a generative time-series model with expectation-maximization and then performs counterfactual inference. We evaluate TASC on both simulated and real-world datasets, including policy evaluation and sports prediction. Our results suggest that TASC offers advantages in settings with strong temporal trends and high levels of observation noise.
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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 | Alberto Abadie and Javier Gardeazabal (2003) The economic costs of conflict: A case study of the Basque Country self | 1.000 | 9 | 5 | 100% |
| 2 | Muhammad Amjad, Devavrat Shah, and Dennis Shen (2018) Robust synthetic control | 0.928 | 10 | 6 | 80% |
| 3 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program self | 0.899 | 11 | 4 | 73% |
| 4 | Kay H Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, and St… (2015) Inferring causal impact using bayesian structural time-series models | 0.843 | 3 | 3 | 100% |
| 5 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2015) Comparative politics and the synthetic control method self | 0.737 | 3 | 2 | 100% |
| 6 | Alberto Abadie and Jérémy L’Hour (2021) A penalized synthetic control estimator for disaggregated data self | 0.644 | 2 | 2 | 100% |
| 7 | Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Kh… (2021) Matrix completion methods for causal panel data models | 0.644 | 2 | 2 | 100% |
| 8 | Nikolay Doudchenko and Guido W Imbens (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis, 2016 | 0.511 | 2 | 1 | 100% |
| 9 | Muhammad Amjad, Vishal Misra, Devavrat Shah, and Dennis Shen (2019) mRSC: Multi-dimensional robust synthetic control self | 0.511 | 2 | 1 | 100% |
| 10 | Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, and Vishal… (2025) Clustersc: Advancing synthetic control with donor selection self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 28 scored citations.