Single proxy synthetic control

Park Chan () and Tchetgen Tchetgen Eric J. ()
Additional contact information
Park Chan: Department of Statistics, University of Illinois Urbana-Champaign, Champaign, Illinois, United States
Tchetgen Tchetgen Eric J.: Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States

Journal of Causal Inference, 2025, vol. 13, issue 1, 22

Abstract: 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.

Keywords: average treatment effect on the treated; conformal inference; generalized method of moments; prediction interval; synthetic control (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2023-0079 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:13:y:2025:i:1:p:22:n:1001

DOI: 10.1515/jci-2023-0079

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().