Single proxy synthetic control
Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time-series settings. A common approach to estimate synthetic control weights is to regress the treated unit’s pretreatment outcome and covariates’ time series measurements on those of untreated un...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-07-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2023-0079 |
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| Summary: | Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time-series settings. A common approach to estimate synthetic control weights is to regress the treated unit’s pretreatment outcome and covariates’ time series measurements on those of untreated units via ordinary least squares. However, this approach can perform poorly if the pretreatment fit is not near perfect, whether the weights are normalized. In this article, we introduce a single proxy synthetic control approach, which views the outcomes of untreated units as proxies of the treatment-free potential outcome of the treated unit, a perspective we leverage to construct a valid synthetic control. Under this framework, we establish an alternative identification strategy and corresponding estimation methods for synthetic controls and the treatment effect on the treated unit. Notably, unlike existing proximal synthetic control methods, which require two types of proxies for identification, ours relies on a single type of proxy, thus facilitating its practical relevance. ∣In addition, we adapt a conformal inference approach to perform inference about the treatment effect, obviating the need for a large number of posttreatment observations. Finally, our framework can accommodate time-varying covariates and nonlinear models. We demonstrate the proposed approach in a simulation study and a real-world application. |
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| ISSN: | 2193-3685 |