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The Augmented Synthetic Control Method
Open accessAbstract
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pretreatment fit is infeasible. Analogous to bias correction for inexact matching, augmented SCM uses an outcome model to estimate the bias due to imperfect pretreatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pretreatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data-generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package.
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“We followed standard practice for placebo tests, conducting in-space and in-time tests.40,42,43”
From The Peacemaker Fellowship and Urban Gun Violence in Richmond, California (2026) · cites this paper as [40]
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“This underscores the importance of the recommendation in Abadie, Diamond, and Hainmueller (2010, 2015) to use SCM only in settings with excellent pretreatment fit.”
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MethodComparative Politics and the Synthetic Control Method
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Comparative Politics and the Synthetic Control MethodPreprint
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“This underscores the importance of the recommendation in Abadie, Diamond, and Hainmueller (2010, 2015) to use SCM only in settings with excellent pretreatment fit.”
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Tax incentives and environmental performance: The pay-as-you-throw policy in Emilia-Romagna, Italy
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International Environmental Agreement and Trade in Environmental Goods: The Case of Kyoto Protocol
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“the emission reductions would not have 36 More details about this method could be referred to Ben-Michael et al. (2021).”
BackgroundMethodInfluentialMora‐Sanguinetti, J. S., & Spruk, R. (2022) · Journal of Regional Science · doi:10.1111/jors.12616 · Cited by 12
Same Root Different Leaves: Time Series and Cross‐Sectional Methods in Panel Data
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“In contrast to Arkhangelsky et al. (2021), Ben-Michael et al. (2021) learns α̂ via ridge regression and β̂ via simplex regression.”
BackgroundMethodInfluentialYears of life lost to revolution and war in Iran
Farzanegan, M. R. (2023) · Review of Development Economics · doi:10.1111/rode.13030 · Cited by 11
Currency Devaluation as a Source of Growth in Africa: A Synthetic Control Approach
Bouvet, F., Bower, R., & Jones, J. (2022) · Eastern Economic Journal · doi:10.1057/s41302-022-00211-4 · Cited by 12
“To assess the impact of the 1994 IMF-supported devaluation on per capita income levels in CFA-zone countries, the augmented synthetic control method (ASCM) proposed by Ben-Michael et al. (2021) is employed.”
MethodRizzo, M. (2025) · Annals of Neurology · doi:10.1002/ana.27230 · Cited by 13
A Design-Based Perspective on Synthetic Control Methods
Bottmer, L., Imbens, G. W., Spiess, J., & Warnick, M. (2023) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2023.2238788 · Cited by 26
Does the Selective Erasure of Protected Areas Raise Deforestation in the Brazilian Amazon?
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“Given that a credible counterfactual is critical, following synthetic-control literature (Arkhangelsky et al. 2021; Ben-Michael et al. 2021) we match units based on pretreatment forest losses.”
BackgroundWhat, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning
Shi, B., Mao, X., Yang, M., & Li, B. (2025) · Information Systems Research · doi:10.1287/isre.2024.0888 · Cited by 10
I will survive! The impact of place-based policies when public transfers fade out
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“We then use the traditional SCM (adapted for the case of multiple treated units) and the Ridge augmented SCM proposed by Ben-Michael et al. (2021).”
MethodWinters, M., Christie, S., Lepage, C., Malik, A. A., Bokemper, S., Abeyesekera, S., Boye, B., Moini, M., Jamil, Z., Tariq, T., et al. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002357 · Cited by 10
“We used an augmented synthetic control method [26, 27], utilizing the GeoLift tool from Facebook to run simulations to identify a combination of districts that would produce a well-powered study.”
MethodThe Effect of Natural Disasters on Inbound Tourism: Synthetic Control Evidence From Italy
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Winter-spring droughts exacerbated PM2.5-O3 compound pollution? Evidence from China
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Who expands the human creative frontier with generative AI: Hive minds or masterminds?
Zhou, E., Lee, D., & Gu, B. (2025) · Science Advances · doi:10.1126/sciadv.adu5800 · Cited by 14
Oliphant, S. N. (2022) · Criminology & Public Policy · doi:10.1111/1745-9133.12601 · Cited by 10
Flood Risk Mapping and the Distributional Impacts of Climate Information
Weill, J. (2023) · American Economic Journal Economic Policy · doi:10.1257/pol.20230552 · Cited by 10
American treasure and the decline of Spain
Charotti, C. J., Palma, N., & dos Santos, J. P. (2025) · European Economic Review · doi:10.1016/j.euroecorev.2025.105187 · Cited by 10
Combining Matching and Synthetic Control to Trade off Biases from Extrapolation and Interpolation
Kellogg, M., Mogstad, M., Pouliot, G., & Torgovitsky, A. (2020) · National Bureau of Economic Research · doi:10.3386/w26624 · Cited by 11
Cerqua, A., Montalbano, P., & Temerbulatova, Z. (2024) · International Economics · doi:10.1016/j.inteco.2024.100506 · Cited by 9
Papadogeorgou, G., Menchetti, F., Choirat, C., Wasfy, J. H., Zigler, C. M., & Mealli, F. (2023) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-022-00294-8 · Cited by 9
“We therefore adopt the augmented synthetic control method proposed in Ben-Michael et al. [2021], which relaxes both the adding-up and the non-negativity restrictions and augments the common synthetic control approach with an outcome model that allows to remove the bias due to an imperfect pre-treatment fit. This metho…”
MethodInfluentialCausal Inference with Time-Series Cross-Sectional Data
Xu, Y. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780192868282.013.30 · Cited by 40
The augmented synthetic control method in public health and biomedical research
Krajewski, T., & Hudgens, M. G. (2024) · Statistical Methods in Medical Research · doi:10.1177/09622802231224638 · Cited by 9
“13 While SCM requires the synthetic control to closely reproduce the trend in the treated unit’s outcome in the pre-treatment period, ASCM allows for situations where a close fit between the synthetic control and the treated unit in the pre-treatment period is infeasible.”
BackgroundMethodResultInfluentialThe Effects of the Flint water crisis on the educational outcomes of school-age children
Trejo, S., Yeomans‐Maldonado, G., & Jacob, B. A. (2024) · Science Advances · doi:10.1126/sciadv.adk4737 · Cited by 12
“For example, our approach is equivalent to using augmented SCM in the specific case where each unit’s pretreatment mean is used as a covariate with the coefficient constrained to be equal to one ( 13 ).”
BackgroundMethodWei, B.-H., Zhao, C.-K., Yan, J.-C., & Li, J. (2025) · Environmental impact assessment review · doi:10.1016/j.eiar.2025.107934 · Cited by 9
The impact of technology finance pilot policies on the quality of urban entrepreneurship in China
Du, Y., Liang, S., Ma, Z., & Xu, Z. (2025) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2025.102785 · Cited by 9
Hierarchically Regularized Entropy Balancing
Xu, Y., & Yang, E. (2021) · Political Analysis · doi:10.1017/pan.2022.12 · Cited by 9
Assessing the Impact of Offshore Wind Power Deployment on Fishery: A Synthetic Control Approach
Shimada, H., Asano, K., Nagai, Y., & Ozawa, A. (2022) · Environmental and Resource Economics · doi:10.1007/s10640-022-00710-0 · Cited by 9
Quantifying the Impact of the Tokyo Olympics on COVID-19 Cases Using Synthetic Control Methods
Esaka, T., & Fujii, T. (2022) · Journal of the Japanese and International Economies · doi:10.1016/j.jjie.2022.101228 · Cited by 9
“(2021) study the impacts of hosting the United States National Football League games and NCAA football games, Carlin et al. (2021) study the impacts of hosting the NHL hockey games, NBA basketball games, and NCAA basketball games, and Dave et al. (2021) study the impact of the Sturgis Motorcycle Rally in South Dakota.”
BackgroundInfluentialDoes E-Procurement Matter for Economic Growth? Subnational Evidence from Australia
Emery, T., Mélon, L., & Spruk, R. (2020) · The Quarterly Review of Economics and Finance · doi:10.1016/j.qref.2022.09.005 · Cited by 12
Panagiotoglou, D., & Lim, J. (2022) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2022.103881 · Cited by 9
Economic Costs of Civil Conflicts: The Case of Burundi
Ndoricimpa, A., & Ndayikeza, M. A. (2023) · Defence and Peace Economics · doi:10.1080/10242694.2023.2232971 · Cited by 9
“The restricted size of the final donor pool precludes the computation of p-values as shown in Abadie, Diomond, and Hainmuller (2015) and impedes the implementation of the relatively recent Augmented Synthetic Control method (Ben-Michael, Feller, and Rothstein 2021).”
AACSB accreditation and student demand
Cameron, M., McCannon, B. C., & Starr, K. (2023) · Southern Economic Journal · doi:10.1002/soej.12660 · Cited by 9
Bridging the gap: Assessing the effects of railway infrastructure investments in Northwest China
Yu, L. B., Tran, T. M., & Lee, W.-S. (2023) · China Economic Review · doi:10.1016/j.chieco.2023.102076 · Cited by 9
Kennedy‐Shaffer, L. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae019 · Cited by 14
“Several extensions of SCM—including the generalized SCM, 34 augmented SCM (see Figure 1D), 35 and Bayesian structural time series model 36 —trade off some interpretability by allowing outcome modeling and extrapolation outside of the convex hull.”
MethodEvaluating Firearm Violence After New Jersey’s Cash Bail Reform
Jahn, J. L., Simes, J. T., & Jay, J. S. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.12535 · Cited by 10
RNN-Based Counterfactual Prediction, With an Application to Homestead Policy and Public Schooling
Poulos, J., & Zeng, S. (2021) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12511 · Cited by 9
“…adoption, where the time of initial treatment varies across multiple treated units (Dube and Zipperer, 2015; Ben-Michael et al., 2019), and to include features of DID estimation (Ben-Michael et al., 2018; Arkhangelsky et al., 2019) or Bayesian estimation (Brodersen et al., 2015; Pang et al., 2020).”
BackgroundChanges in prenatal care and birth outcomes after federally qualified health center expansion
Gourevitch, R. A., & Hatfield, L. A. (2022) · Health Services Research · doi:10.1111/1475-6773.14099 · Cited by 9
Benedetti, M. H., Lü, B., Kinnear, N., Li, L., Delgado, M. K., & Zhu, M. (2022) · Journal of Safety Research · doi:10.1016/j.jsr.2022.11.003 · Cited by 9
Liu, H., Kim, D., Li, H., Rodgers, M. O., & Guensler, R. L. (2022) · Transportation · doi:10.1007/s11116-022-10350-4 · Cited by 9
“…that recent studies have tried to lose the interpolation restriction, i.e., prohibiting extrapolation by setting the weight factor between 0 and 1 (Ben-Michael et al. 2021), which might be an option for the states such as Louisiana and Vermont that are “unique” in terms of PEV sales and…”
Multivariate Statistical Process Control
Kenett, R. S., Zacks, S., & Gedeck, P. (2023) · Statistics for industry, technology, and engineering · doi:10.1007/978-3-031-28482-3_4 · Cited by 9
Aqpet — An R package for air quality policy evaluation
Dai, Y., Liu, B., Tong, C., & Shi, Z. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106052 · Cited by 8
Guo, J., Wang, T., Cao, H. Q., Ma, Q., Tang, Y., Li, T., Wang, L., Xu, Y., & Zhan, S. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.111737 · Cited by 9
Augmented balancing weights as linear regression
Bruns-Smith, D., Dukes, O., Feller, A., & Ogburn, E. L. (2025) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkaf019 · Cited by 8
“…a number of terms, including augmented balancing weights (Athey et al., 2018; Hirshberg and Wager, 2021), automatic debiased machine learning (AutoDML; Chernozhukov et al., 2022d), and generalized regression estimators (GREG; Deville and Särndal, 1992); see Ben-Michael et al. (2021b) for a review.”
BackgroundMethodInfluentialSelf-citationDynamic treatment effect estimation with interactive fixed effects and short panels
Brown, N. L., & Butts, K. (2025) · Journal of Econometrics · doi:10.1016/j.jeconom.2025.106013 · Cited by 8
Aquino, D. H., Domingo, N., & Atapattu, C. N. (2025) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2025.105695 · Cited by 8
Thom, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271063 · Cited by 8
“Placebo tests aid that determination by applying SCM to each donor state and comparing donor-control differences, which are random, to the Californiacontrol difference.”
BackgroundInfluentialEffects of Wisconsin’s handgun waiting period repeal on suicide rates
Oliphant, S. N. (2022) · Injury Prevention · doi:10.1136/ip-2022-044719 · Cited by 8
“23 All statistical analyses were conducted using R V.4.2.1.”
InfluentialCui, Y., Xu, H., An, D., & Yang, L. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1403347 · Cited by 8
Quantile control via random forest
Chen, Q., Xiao, Z., & Yao, Q. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105789 · Cited by 8
Estimating racial disparities in emergency general surgery
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Self-citationAuerbach, J., & Pierson, S. (2021) · Statistics and Public Policy · doi:10.1080/2330443x.2021.1906806 · Cited by 8
“The matrix completion approach builds on the synthetic controls literature (Abadie and Gardeazabal 2003; Abadie, Diamond, and Hainmueller 2010; Ben-Michael, Feller, and Rothstein 2018, 2020) and “unconfoundedness” literature (Rosenbaum 2002, 2010; Imbens and Rubin 2015).”
MethodShi, X., Li, K., Miao, W., Hu, M., & Tchetgen, E. T. (2021) · PubMed Central · doi:10.48550/arxiv.2108.13935 · Cited by 8
“Cattaneo et al. (2021) proposed an alternative approach for obtaining prediction intervals one might potentially consider in combination with our PCI framework.”
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Further improvement in London’s air quality demands more than the Ultra Low Emission Zone policy
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“Following Ben-Michael et al. 23 the Ridge ASCM approach is applied in this study.”
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Political signalling and emissions trading schemes in China: Insights from Guangdong Province
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Grossi, G., Mariani, M., Mattei, A., Lattarulo, P., & Öner, Ö. (2024) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnae032 · Cited by 8
The local economic impacts of mega nuclear accident: A synthetic control analysis of Fukushima
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The unseen toll: excess mortality during covid-19 lockdowns
Ege, F., Mellace, G., & Menon, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-45934-2 · Cited by 7
“Second, we show that our results are consistent by implementing two additional state of the art methods: the augmented synthetic control 12 and the synthetic difference-in-differences 13 .”
MethodResultInfluentialComputing Synthetic Controls Using Bilevel Optimization
Malo, P., Eskelinen, J., Zhou, X. Y., & Kuosmanen, T. (2023) · Computational Economics · doi:10.1007/s10614-023-10471-7 · Cited by 8
“These observations can help to explain the empirical observation that the predictors often turn out to have little impact on the synthetic control, which has been noted by several authors (e.g., Ben-Michael et al., 2021; Doudchenko & Imbens 2017; Kaul et al., 2022).”
Financial and Informational Integration Through Oracle NetworksPreprint
Cong, L. W., Prasad, E. S., & Rabetti, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4495514 · Cited by 7
Huy, T. N., Adjognon, G. S., & Soest, D. P. V. (2024) · American Journal of Agricultural Economics · doi:10.1111/ajae.12464 · Cited by 7
Evaluation of the causal impact of recreational marijuana legalisation on traffic safety in the US
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Lu, B., Ben‐Michael, E., Feller, A., & Miratrix, L. W. (2023) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986231155427 · Cited by 6
Self-citationEstimating the Effects of Political Instability in Nascent Democracies
Emery, T., Kovač, M., & Spruk, R. (2023) · Jahrbücher für Nationalökonomie und Statistik · doi:10.1515/jbnst-2022-0074 · Cited by 6
Yan, Z., & Liao, L. (2025) · Economic Analysis and Policy · doi:10.1016/j.eap.2025.01.022 · Cited by 7
School reopenings, COVID-19, and employment
Koppa, V., & West, J. (2022) · Economics Letters · doi:10.1016/j.econlet.2022.110310 · Cited by 6
“In Appendix C, e address this concern by showing that results are robust to using synthetic ontrols for each county (Ben-Michael et al., 2021).”
BackgroundSynthetic Control with Time Varying Coefficients A State Space Approach with Bayesian Shrinkage
Klinenberg, D. (2022) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2022.2102025 · Cited by 8
A review about COVID-19 in the MENA region: environmental concerns and machine learning applications
Meskher, H., Belhouari, S. B., Thakur, A. K., Sathyamurthy, R., Singh, P., Khelfaoui, I., & Saidur, R. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-23392-z · Cited by 6
“As presented in figure S1, several metrological and non-met-rological conditions influence the COVID-19 pandemic in the MENA region (Chen et al. 2020a, b; Mansouri Daneshvar et al. 2022; Bentout et al. 2021; Lodder et al. 2020).”
BackgroundApproximate balancing weights for clustered observational study designs
Ben‐Michael, E., Page, L. C., & Keele, L. J. (2024) · Statistics in Medicine · doi:10.1002/sim.10054 · Cited by 7
Self-citationImputation of Counterfactual Outcomes when the Errors are Predictable
Gonçalves, Ś., & Ng, S. (2024) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2024.2358900 · Cited by 6
Schuldenbremse und öffentliche Investitionen: Erwiderung auf Mühlenweg et al. (2024)
Feld, L. P., Hassib, J., Langer, M., Nientiedt, D., & Weber, P. (2024) · Wirtschaftsdienst · doi:10.2478/wd-2024-0123 · Cited by 6
Die Schuldenbremse – ein Garant für nachhaltige Haushaltspolitik? Eine Replik auf Feld et al. (2024)
Mühlenweg, L., Kaczmarczyk, P., Hornung, L. J., & Kleimeier, N. (2024) · Wirtschaftsdienst · doi:10.2478/wd-2024-0122 · Cited by 6
Minimum Wage Effects and Monopsony Explanations
Wiltshire, J. C., McPherson, C., Reich, M., & Sosinskiy, D. (2025) · Journal of Labor Economics · doi:10.1086/735551 · Cited by 6
Learning Treatment Effects in Panels with General Intervention PatternsPreprint
Farias, V. F., Li, A. A., & Peng, T. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2106.02780 · Cited by 7
“[7, 8, 9] consider a variety of regularized regression techniques to learn the linear combination of untreated units that yields a synthetic control.”
BackgroundMind Thy Neighbor's Portfolio: A Network Approach to Contagion in the ETF MarketPreprint
Iwadate, B. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3535942 · Cited by 6
Spiess, J., Imbens, G. W., & Venugopal, A. (2023) · National Bureau of Economic Research · doi:10.3386/w31802 · Cited by 6
“Thereby, we also relate to work on high-dimensional and regularized synthetic control (Doudchenko and Imbens, 2016; Abadie and L’Hour, 2021; Ben-Michael et al., 2021) and the connections between synthetic control and linear regression (Athey et al.”
MethodOn the impact of next generation EU funds: A regional synthetic control method approach
Aparicio‐Pérez, D., Espinosa, P., Pavía, J. M., & Tortosa‐Ausina, E. (2025) · Papers of the Regional Science Association · doi:10.1016/j.pirs.2025.100082 · Cited by 5
Populist constitutional backsliding and judicial independence: Evidence from Türkiye
Garoupa, N. M., & Spruk, R. (2025) · International Review of Law and Economics · doi:10.1016/j.irle.2025.106301 · Cited by 9
Obstetric-Related Emergency Medical Treatment and Labor Act Violations and No Health Exception Bans
Woskie, L., Brower, N., Shaffer, J. D., & Ladin, K. (2025) · JAMA Health Forum · doi:10.1001/jamahealthforum.2025.4726 · Cited by 5
Psilocybin Trends in States That Decriminalized Use
Black, J. C., Bau, G. E., Cook, R. R., Bemis, E. A., & Monte, A. A. (2026) · JAMA · doi:10.1001/jama.2026.1952 · Cited by 5
Suntay, O. (2024) · Comparative Political Studies · doi:10.1177/00104140241252077 · Cited by 5
“Therefore, a novel augmented synthetic method offering a solution for the poor matching of pretreatment variables (by using an outcome model to de-bias the estimate) developed by Ben-Michael et al. (2021)is used in this study to create a suitable synthetic UK.”
The mitigating effect of masks on the spread of Covid-19
Cooper, D. H., Garga, V., Luengo-Prado, M., & Tang, J. (2022) · Economics & Human Biology · doi:10.1016/j.ehb.2022.101195 · Cited by 5
“Bias correction methods have also been developed to take into account discrepancies between the predictor variables for each treated unit and its ‘‘donor’’ units (Abadie and L’Hour, 2021; Ben-Michael et al., 2021), and inference typically occurs through calculating p-values based on placebo tests in which treatment st…”
MethodInfluentialState Laws That Require Coprescribing Opioids and Naloxone and Codispensing Practices
Tormohlen, K. N., Schmid, I. T., Stuart, E. A., Davis, C. S., & McGinty, E. E. (2023) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2023.09.016 · Cited by 5
Griffin, B. A., Schuler, M. S., Stone, E. M., Patrick, S. W., Stein, B. D., de Lima, P. N., Griswold, M., Scherling, A., & Stuart, E. A. (2023) · Epidemiology · doi:10.1097/ede.0000000000001659 · Cited by 5
“14 Lastly, we examined the doubly robust DID approach proposed in Callaway and Sant’Anna, 15 developed to overcome the known bias in the classic two-way fixed effects model in the presence of treatment effect heterogeneity and staggered adoption.”
MethodUnraveling the effects of import bans on domestic poultry production: a case study of Senegal
Zamani, O., Chibanda, C., & Pelikan, J. (2023) · Agricultural and Food Economics · doi:10.1186/s40100-023-00283-6 · Cited by 6
“The SCM is based on the idea that a weighted average of control units in the donor pool ( X N 1, t ) represents a better counterfactual than a single unit (Demko and Jaenicke 2018; Ben-Michael et al. 2021).”
BackgroundDid private election administration funding advantage Democrats in 2020?
Lal, A., & Thompson, D. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317563121 · Cited by 5
Confidence intervals of treatment effects in panel data models with interactive fixed effects
Li, X., Shen, Y., & Zhou, Q. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105684 · Cited by 6
Active matrix factorization for surveys
Zhang, C., Taylor, S. J., Cobb, C., & Sekhon, J. S. (2020) · The Annals of Applied Statistics · doi:10.1214/20-aoas1322 · Cited by 5
Representation and Forest Conservation: Evidence from India’s Scheduled AreasPreprint
Lal, A., Gulzar, S., & Pasquale, B. (2021) · doi:10.31235/osf.io/3uvmc · Cited by 5
He, Z., Chu, B. B., Yang, J., Gu, J., Chen, Z., Liu, L., Morrison, T., Belloy, M. E., Qi, X., Hejazi, N. S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.28.582621 · Cited by 5
Rebeiro, P. F., Thome, J. C., Gange, S. J., Althoff, K. N., Berry, S. A., Horberg, M. A., Moore, R. D., Silverberg, M. J., Sack, D. E., Sterling, T. R., et al. (2024) · Health Affairs Scholar · doi:10.1093/haschl/qxae128 · Cited by 4
Dai, Y., Liu, B., Tong, C., Carslaw, D. C., MacKenzie, A. R., & Shi, Z. (2025) · Atmospheric chemistry and physics · doi:10.5194/acp-25-13585-2025 · Cited by 5
Cao, Y., Hanafiah, M. H., Ghazali, M. F., Razak, R. A., & Zheng, Y. (2025) · Sustainability · doi:10.3390/su18010281 · Cited by 4
Metadata
- Authors
- Ben-Michael, E., Feller, A., & Rothstein, J.
- Affiliations
- Department of Statistics, Institute for Quantitative Social Sciences, Harvard University
- University of California System (US)
- Corresponding author
- Feller, Avi
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2021-07-29
- Volume
- 116
- Issue
- 536
- Pages
- 1789-1803
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W3123564515
- Cited by
- 462
- References
- 42
- Citations by year
- 2026: 93 · 2025: 110 · 2024: 79 · 2023: 69 · 2022: 58 · 2021: 33
- FWCI
- 44.66
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of the American Statistical Association (publishedVersion)
- National Bureau of Economic Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion)
- National Bureau of Economic Research (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Figshare (cc-by)
- Funders
- Institute of Education Sciences (R305D200010)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Keywords
- synthetic control method, treatment effect estimation, bias correction, ridge regression, factor models, regularization, overfitting, economic growth
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