Robust Synthetic Control

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   Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy

   Yanlin Zhang, Sung-Yong Chung, Alireza Talebpour · 2026 · Pas encore résumé

   Résumé scientifique

   Synthetic control methods estimate counterfactual outcomes by weighting untreated donor units to match a treated unit before treatment. In recent applications, however, the donor pool can be large, highly correlated, and short in pre-treatment history, yielding many near-equivalent solutions with unstable and difficult-to-interpret donor weights. We propose Graph-Regularized Synthetic Control (Graph-SC), a convex estimator that augments the classical pre-treatment matching objective with a graph Laplacian penalty. The graph encodes similarity among donor trajectories, encouraging coherent weights over structurally related donors while preserving the transparency of synthetic controls. We pro…

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   Differentially Private Synthetic Control

   Saeyoung Rho, Rachel Cummings, V. Misra · 2023 · International Conference on Artificial Intelligence and Statistics · cité 3 fois · Pas encore résumé

   Résumé scientifique

   Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool) to predict a counterfactual time series of interest (i.e., target unit) by analyzing the relationship between the target and the donor pool before the intervention. As synthetic control tools are increasingly applied to sensitive or proprietary data, formal privacy protections are often required. In this work, we provide the first algorithms for differentially private synthetic control with explicit error bounds. Our approach builds upon tools from non-private sy…

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   Cherry Picking with Synthetic Controls

   Bruno Ferman, Cristine Pinto, V. Possebom · 2020 · Journal of Policy Analysis and Management · cité 252 fois · Pas encore résumé

   Résumé scientifique

   The synthetic control (SC) method has been recently proposed as an alternative method to estimate treatment e ects in comparative case studies. Abadie et al. [2010] and Abadie et al. [2015] argue that one of the advantages of the SC method is that it imposes a data-driven process to select the comparison units, providing more transparency and less discretionary power to the researcher. However, an important limitation of the SC method is that it does not provide clear guidance on the choice of predictor variables used to estimate the SC weights. We show that such lack of speci c guidances provides signi cant opportunities for the researcher to search for speci cations with statistically sign…

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   Tactics for design and inference in synthetic control studies: An applied example using high-dimensional data

   Alex Hollingsworth, Coady Wing · 2020 · Social Science Research Network · cité 52 fois · Pas encore résumé

   Résumé scientifique

   We describe identification assumptions underlying synthetic control studies and offer recommendations for key---and normally ad hoc---implementation decisions, focusing on model selection; model fit; cross-validation; and decision rules for inference. We outline how to implement a Synthetic Control Using Lasso (SCUL). The method---available as an R package---allows for a high-dimensional donor pool; automates model selection; includes donors from a wide range of variable types; and permits both extrapolation and negative weights. In an application, we employ our recommendations and the SCUL strategy to estimate how recreational marijuana legalization affects sales of alcohol and over-the-cou…

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   Single proxy synthetic control

   Chan Park, E. T. Tchetgen Tchetgen · 2023 · Journal of Causal Inference · cité 2 fois · Pas encore résumé

   Résumé scientifique

   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 …

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   A regularized approach to the synthetic control method: an empirical evaluation

   Tom Stolp, I. Cornelisz, C. van Klaveren · 2026 · Empirical Economics · Pas encore résumé

   Résumé scientifique

   The synthetic control method (SCM) estimates the treatment effect for a single treated unit by constructing a counterfactual outcome as a weighted average of the outcomes from untreated donor units. SCM requires that the estimated weights be greater than zero and sum to one. However, these constraints can be suboptimal if the treated unit's time series exhibits consistently higher or lower values or variance, or opposite trends relative to the donor units. To address this limitation, we systematically evaluate a regularized version of SCM (RSCM), allowing weights to take on any value. Simulation results show that SCM and RSCM perform equally well when outcomes of the treated unit are located…

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   Splash! Robustifying Donor Pools for Policy Studies

   Jared Greathouse, M. Bayani, Jason Coupet · 2023 · cité 1 fois · Pas encore résumé

   Résumé scientifique

   Policy researchers using synthetic control methods typically choose a donor pool in part by using policy domain expertise so the untreated units are most like the treated unit in the pre intervention period. This potentially leaves estimation open to biases, especially when researchers have many potential donors. We compare how functional principal component analysis synthetic control, forward-selection, and the original synthetic control method select donors. To do this, we use Gaussian Process simulations as well as policy case studies from West German Reunification, a hotel moratorium in Barcelona, and a sugar-sweetened beverage tax in San Francisco. We then summarize the implications for…

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   A Bayesian Alternative to Synthetic Control for Comparative Case Studies

   Xun Pang, Li-Cheng Liu, Yi-Qing Xu · 2020 · Political Analysis · cité 61 fois · Pas encore résumé

   Résumé scientifique

   Abstract This paper proposes a Bayesian alternative to the synthetic control method for comparative case studies with a single or multiple treated units. We adopt a Bayesian posterior predictive approach to Rubin’s causal model, which allows researchers to make inferences about both individual and average treatment effects on treated observations based on the empirical posterior distributions of their counterfactuals. The prediction model we develop is a dynamic multilevel model with a latent factor term to correct biases induced by unit-specific time trends. It also considers heterogeneous and dynamic relationships between covariates and the outcome, thus improving precision of the causal e…

Les travaux antérieurs sur lesquels il s’appuie

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   Synth: An R Package for Synthetic Control Methods in Comparative Case Studies

   Alberto Abadie, A. Diamond, J. Hainmueller · 2011 · Journal of Statistical Software · cité 417 fois · Pas encore résumé

   Résumé scientifique

   The R package Synth implements synthetic control methods for comparative case studies designed to estimate the causal effects of policy interventions and other events of interest (Abadie and Gardeazabal 2003; Abadie, Diamond, and Hainmueller 2010). These techniques are particularly well-suited to investigate events occurring at an aggregate level (i.e., countries, cities, regions, etc.) and affecting a relatively small number of units. Benefits and features of the Synth package are illustrated using data from Abadie and Gardeazabal (2003), which examined the economic impact of the terrorist conflict in the Basque Country.

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   Comparative Politics and the Synthetic Control Method

   Alberto Abadie, A. Diamond, J. Hainmueller · 2014 · American Journal of Political Science · cité 2 218 fois · Pas encore résumé
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   Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

   Yiqing Xu · 2017 · Political Analysis · cité 922 fois · Pas encore résumé
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   Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units

   N. Kreif, R. Grieve, Dominik Hangartner et al. · 2015 · Health Economics · cité 309 fois · Pas encore résumé

   Résumé scientifique

   Abstract This paper examines the synthetic control method in contrast to commonly used difference‐in‐differences (DiD) estimation, in the context of a re‐evaluation of a pay‐for‐performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re‐weighting the control group so that it has similar pre‐int…

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   An Empirical Comparison Between the Synthetic Control Method and HSIAO et al.'s Panel Data Approach to Program Evaluation

   J. Gardeazabal, Ainhoa Vega-Bayo · 2017 · Journal of Applied Econometrics · cité 95 fois · Pas encore résumé

Les articles qui le citent

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   Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects

   Alberto Abadie · 2021 · Journal of Economic Literature · cité 1 301 fois · Pas encore résumé

   Résumé scientifique

   Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research. (JEL B41, C32, C54, E2…

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   An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

   V. Chernozhukov, Kaspar Wüthrich, Yin-Chu Zhu · 2017 · Journal of the American Statistical Association · cité 266 fois · Pas encore résumé

   Résumé scientifique

   Abstract We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic con…

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   Synthetic controls with imperfect pretreatment fit

   Bruno Ferman, Cristine Pinto · 2016 · Quantitative Economics · cité 212 fois · Pas encore résumé

   Résumé scientifique

   We analyze the properties of the Synthetic Control (SC) and related estimators when the pre‐treatment fit is imperfect. In this framework, we show that these estimators are generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre‐treatment periods goes to infinity. Still, we show that a demeaned version of the SC method can improve in terms of bias and variance relative to the difference‐in‐difference estimator. We also derive a specification test for the demeaned SC estimator in this setting with imperfect pre‐treatment fit. Given our theoretical results, we provide practical guidance for applied researchers on how to justify the use of…

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   ClusterSC: Advancing Synthetic Control with Donor Selection

   Saeyoung Rho, Andrew Tang, Noah Bergam et al. · 2025 · International Conference on Artificial Intelligence and Statistics · cité 1 fois · Pas encore résumé

   Résumé scientifique

   In causal inference with observational studies, synthetic control (SC) has emerged as a prominent tool. SC has traditionally been applied to aggregate-level datasets, but more recent work has extended its use to individual-level data. As they contain a greater number of observed units, this shift introduces the curse of dimensionality to SC. To address this, we propose Cluster Synthetic Control (ClusterSC), based on the idea that groups of individuals may exist where behavior aligns internally but diverges between groups. ClusterSC incorporates a clustering step to select only the relevant donors for the target. We provide theoretical guarantees on the improvements induced by ClusterSC, supp…

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   Imperfect Synthetic Controls

   David Powell · 2026 · Journal of applied econometrics · Pas encore résumé

   Résumé scientifique

   The synthetic control method assumes the existence of a perfect synthetic control, which cannot exist if the outcomes are functions of transitory shocks with nonzero asymptotic variance and may not exist even in expectation for the treated unit. This paper first shows the benefits of estimating synthetic controls for all units. If the treated unit composes part of the synthetic control for any untreated unit, the treatment effect is independently identified by the synthetic outcome minus the outcome of the untreated unit in the post-treatment period (divided by the synthetic control weight on the treated unit outcome). This paper introduces an estimator which generates synthetic controls for…