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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03099 |
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| _version_ | 1866915711160418304 |
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| author | Rho, Saeyoung Illick, Cyrus Narasipura, Samhitha Abadie, Alberto Hsu, Daniel Misra, Vishal |
| author_facet | Rho, Saeyoung Illick, Cyrus Narasipura, Samhitha Abadie, Alberto Hsu, Daniel Misra, Vishal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03099 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Time-Aware Synthetic Control Rho, Saeyoung Illick, Cyrus Narasipura, Samhitha Abadie, Alberto Hsu, Daniel Misra, Vishal Machine Learning Econometrics 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. |
| title | Time-Aware Synthetic Control |
| topic | Machine Learning Econometrics |
| url | https://arxiv.org/abs/2601.03099 |