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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Standard practice for placebo tests was followed by conducting in-space and in-time tests.
“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]
Section 2.3 describes placebo checks as part of synthetic-control practice, and the appendix discussion of Figures F.10–F.12 reports in-time placebo tests. The passages do not establish in-space tests as part of the practice attributed to this source. Whether the citing authors conducted either test is their own report, not something this source can verify. (LLM, conf=0.83)
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“Ben-Michael et al. (2018) derive finite-sample bounds on the bias of the SC estimator, and show that the bounds they derive do not converge to zero when J is fixed and T0 ! 1.”
BackgroundInfluentialAn Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
Chernozhukov, V., Wüthrich, K., & Zhu, Y. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1920957 · Cited by 176 · see citing works
“Our inference method also works in conjunction with more recently proposed modified versions of SC, such as the augmented SC estimator of Ben-Michael, Feller, and Rothstein (2018).”
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“Methodologically, our work draws most directly from the literature on SC meth-ods, including Abadie and Gardeazabal ( 2003 ) ; Abadie, Diamond, and Hainmueller ( 2010, 2015 ; Abadie and L’Hour ( 2016 ) ; Doudchenko and Imbens ( 2016 ) ; and Ben-Michael, Feller, and Rothstein ( 2018 ) .”
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BackgroundMethodDo Renewable Portfolio Standards Deliver Cost-Effective Carbon Abatement?Preprint
Greenstone, M., & Nath, I. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3374942 · Cited by 44 · see citing works
Doucette, M. L., Ward, J. A., McCourt, A. D., Webster, D. W., & Crifasi, C. K. (2022) · Journal of Urban Health · doi:10.1007/s11524-022-00627-5 · Cited by 44 · see citing works
Texas’ 2021 Ban on Abortion in Early Pregnancy and Changes in Live Births
Bell, S. O., Stuart, E. A., & Gemmill, A. (2023) · JAMA · doi:10.1001/jama.2023.12034 · Cited by 39 · see citing works
A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data
Ben‐Michael, E., Feller, A., & Stuart, E. A. (2021) · Epidemiology · doi:10.1097/ede.0000000000001369 · Cited by 40 · see citing works
Self-citationMesserle, R., & Schreyögg, J. (2023) · The European Journal of Health Economics · doi:10.1007/s10198-023-01645-z · Cited by 37 · see citing works
“Fixest, Augsynth, and SynthDiD were primarily used as packages for estimation [40, 53, 54].”
MethodKaplan, S., White, J. S., Madsen, K. A., Basu, S., Villas-Boas, S. B., & Schillinger, D. (2024) · JAMA Health Forum · doi:10.1001/jamahealthforum.2023.4737 · Cited by 35 · see citing works
Zhang, Z., Deng, H., Yan, M., Feng, C. Q., & Sun, H. (2025) · International Review of Economics & Finance · doi:10.1016/j.iref.2025.104265 · Cited by 34 · see citing works
Liu, L., Wang, Y., & Xu, Y. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3555463 · Cited by 142 · see citing works
The impact of e-procurement on institutional quality
Mélon, L., & Spruk, R. (2020) · Journal of Public Procurement · doi:10.1108/jopp-07-2019-0050 · Cited by 33 · see citing works
Treatment effects in interactive fixed effects models with a small number of time periods
Callaway, B., & Karami, S. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.02.001 · Cited by 38 · see citing works
“Let ATTDID denote the expression on the right hand side of Equation (10) but allowing for the possibility that the parallel trends assumption does not hold (so that, in this case, ATTDID may not be equal to the ATT ).”
BackgroundConsidering multiple governance levels in epidemiologic analysis of public policies
Schnake-Mahl, A. S., Jahn, J. L., Purtle, J., & Bilal, U. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115444 · Cited by 32 · see citing works
Causality in Econometrics: Choice vs Chance
Imbens, G. W. (2022) · Econometrica · doi:10.3982/ecta21204 · Cited by 36 · see citing works
“The basic synthetic control method as well as various extensions (Arkhangelsky, Athey, Hirshberg, Imbens, and Wager (2021), Ben-Michael, Feller, and Rothstein (2021)), have in a short time found many applications in a wide range of fields, including studies of the effects of Brexit and the effects…”
MethodOn the implied weights of linear regression for causal inference
Chattopadhyay, A., & Zubizarreta, J. R. (2022) · Biometrika · doi:10.1093/biomet/asac058 · Cited by 32 · see citing works
Samartsidis, P., Seaman, S. R., Presanis, A. M., Hickman, M., & Angelis, D. D. (2019) · Statistical Science · doi:10.1214/19-sts713 · Cited by 34 · see citing works
Rico, J., Wang, Z., Panlasigui, S., Loucks, C., Swenson, J. J., & Pfaff, A. S. P. (2022) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2022.102780 · Cited by 30 · see citing works
Ham, D. W., & Miratrix, L. W. (2024) · The Annals of Applied Statistics · doi:10.1214/24-aoas1872 · Cited by 31 · see citing works
The PCDID Approach: Difference-in-Differences When Trends Are Potentially Unparallel and Stochastic
Chan, M. K., & Kwok, S. S. M. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2021.1914636 · Cited by 29 · see citing works
“The PCDID approach is related to the large literature on factor-augmented regression models (e.g., Stock and Watson 2002; Bernanke, Boivin, and Eliasz 2005; Bai and Ng 2006), which forecast one or several time series using a large number of predictor series.”
MethodSimionescu, M., Radulescu, M., & Belaşcu, L. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.121397 · Cited by 27 · see citing works
Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity
Masini, R., & Medeiros, M. C. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1964978 · Cited by 27 · see citing works
“See also Amjad, Shah, and Shen (2018), Arkhangelsky et al. (2019), and Ben-Michael, Feller, and Rothstein (2019).”
BackgroundSheridan, P. E., McElroy, S., Casey, J. A., & Benmarhnia, T. (2022) · Epidemiology · doi:10.1097/ede.0000000000001539 · Cited by 26 · see citing works
Multilevel Calibration Weighting for Survey Data
Ben‐Michael, E., Feller, A., & Hartman, E. (2023) · Political Analysis · doi:10.1017/pan.2023.9 · Cited by 26 · see citing works
“7 If we allow unbounded extrapolation and set L = −∞ and U = ∞, the resulting estimator will be equivalent to linear regression weights from a linear regression of the outcome on first-order indicatorsD (1) (Ben-Michael et al. 2021).”
MethodInfluentialSelf-citationDo fiscal rules undermine public investments? A review of empirical evidence
Blesse, S., Dörr, L., Dorn, F., & Lay, M. (2025) · European Journal of Political Economy · doi:10.1016/j.ejpoleco.2025.102775 · Cited by 25 · see citing works
The how and why of Bayesian nonparametric causal inference
Linero, A. R., & Antonelli, J. (2022) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1583 · Cited by 25 · see citing works
“…are two very popular topics Wiesen-farth et al., 2014; Adhikari et al., 2020), to principal stratification (Schwartz et al., 2011), to high-dimensional panel data settings (Antonelli et al., 2020), and to replace synthetic control methods with flexible model-based methods (Ben-Michael et al., 2021).”
MethodMcGinty, E. E., Seewald, N. J., Bandara, S. N., Cerdá, M., Daumit, G. L., Eisenberg, M. D., Griffin, B. A., Igusa, T., Jackson, J. W., Kennedy‐Hendricks, A., et al. (2022) · Prevention Science · doi:10.1007/s11121-022-01427-8 · Cited by 26 · see citing works
Impact of high-speed rail on income inequalities in Italy
Matteo, D. D., & Cardinale, B. (2023) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2023.103652 · Cited by 25 · see citing works
Do price reductions attract customers in urban public transport? A synthetic control approach
Wallimann, H., Blättler, K., & von Arx, W. (2023) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2023.103700 · Cited by 24 · see citing works
Synthetic Controls with Staggered Adoption
Ben‐Michael, E., Feller, A., & Rothstein, J. M. (2021) · National Bureau of Economic Research · doi:10.3386/w28886 · Cited by 24 · see citing works
Self-citationLiu, B., Bryson, J. R., Sevinç, D., Cole, M. A., Elliott, R., Bartington, S., Bloss, W. J., & Shi, Z. (2023) · Environmental and Resource Economics · doi:10.1007/s10640-023-00794-2 · Cited by 23 · see citing works
“Both hourly observed and weather normalised pollutant concentrations were converted into weekly averages to run the second-stage analysis, i.e., the Ridge Augmented Synthetic Control Method (ASCM).”
BackgroundMethodInfluentialCommunity notes reduce engagement with and diffusion of false information online
Slaughter, I., Peytavin, A., Ugander, J. H., & Saveski, M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2503413122 · Cited by 23 · see citing works
Counterfactual Analysis and Inference With Nonstationary Data
Masini, R., & Medeiros, M. C. (2020) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2020.1799814 · Cited by 23 · see citing works
Combining counterfactual outcomes and ARIMA models for policy evaluation
Menchetti, F., Cipollini, F., & Mealli, F. (2022) · Econometrics Journal · doi:10.1093/ectj/utac024 · Cited by 28 · see citing works
Government debt, European Institutions and fiscal rules: a synthetic control approach
Kraemer, R., & Lehtimäki, J. (2023) · International Tax and Public Finance · doi:10.1007/s10797-023-09791-z · Cited by 23 · see citing works
“In applying the SCM, the approach of this paper follows the seminal work of Abadie and Gardeazabal (2003) and Abadie et al. (2010) on the SCM as well as the bias-corrected SCM in line with (Abadie & L’hour, J., 2021; Ben-Michael et al., 2021; Abadie, 2021) as a large-scale robustness analysis.”
Synthetic Control as Online Linear Regression
Chen, J. (2023) · Econometrica · doi:10.3982/ecta20720 · Cited by 22 · see citing works
“…typically derived under a lowrank linear factor model or a vector autoregressive model of the outcomes (see, among others, Abadie et al., 2010, Ben-Michael et al., 2019, 2021, Ferman and Pinto, 2021, Viviano and Bradic, 2019).2 While these statistical guarantees formally hold under these…”
BackgroundLiang, Y., & He, X. (2024) · Economic Analysis and Policy · doi:10.1016/j.eap.2024.10.012 · Cited by 20 · see citing works
Effects of U.S. State Medical Cannabis Laws on Treatment of Chronic Noncancer Pain
McGinty, E. E., Tormohlen, K. N., Seewald, N. J., Bicket, M. C., McCourt, A. D., Rutkow, L., White, S., & Stuart, E. A. (2023) · Annals of Internal Medicine · doi:10.7326/m23-0053 · Cited by 20 · see citing works
“Statistical Analysis We used an augmented synthetic control approach (31), which weights comparison states so that the outcomes during the prelaw period are identical in the intervention and comparison states.”
MethodInfluentialPublic cooperation and the police: Do calls-for-service increase after homicides?
Brantingham, P. J., & Uchida, C. D. (2021) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2021.101785 · Cited by 23 · see citing works
Did Ohio’s Vaccine Lottery Increase Vaccination Rates? A Pre-Registered, Synthetic Control Study
Lang, D., Esbenshade, L., & Willer, R. (2023) · Journal of Experimental Political Science · doi:10.1017/xps.2021.32 · Cited by 31 · see citing works
“2021), and additionally including daily estimates of individual mobility provided by Google trends (Barber and West 2021): Lastly, we varied the modeling technique used to compare the traditional synthetic control approach to an augmented synthetic control model (Ben-Michael et al. 2021a), exploring a technique that r…”
MethodClarke, G. M., Steventon, A., & O’Neill, S. (2022) · Health Services Research · doi:10.1111/1475-6773.14126 · Cited by 19 · see citing works
Samartsidis, P., Seaman, S. R., Montagna, S., Charlett, A., Hickman, M., & Angelis, D. D. (2020) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12569 · Cited by 19 · see citing works
“…time series observational data, including frequentist approaches based on the FA model (Gobillon and Magnac, 2016; Chan and Kwok, 2016; Xu, 2017; Li, 2018) and synthetic control-type approaches (Abadie et al. , 2010; Hsiao et al. , 2012; Doudchenko and Imbens, 2016; Ben-Michael et al. , 2018).”
An Alternative to Synthetic Control for Models with Many Covariates Under Sparsity
Bléhaut, M., D’Haultfœuille, X., L’Hour, J., & Tsybakov, A. B. (2023) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-30114-8_12 · Cited by 18 · see citing works
Samartsidis, P., Seaman, S. R., Presanis, A. M., Hickman, M., & Angelis, D. D. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.40086 · Cited by 18 · see citing works
Synthetic Learner: Model-free inference on treatments over time
Viviano, D., & Bradić, J. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.07.006 · Cited by 18 · see citing works
“Alternatives for SC include ridge-regression (Ben-Michael et al., 2018), kernel balancing (Hazlett and Xu, 2018), structural models with auto-regressive elements (Blackwell and Glynn, 2018), generalized SC (Xu, 2017; Doudchenko and Imbens, 2016).”
MethodGottlieb, A., Harper, T., & Jung, H. (2024) · Law & Policy · doi:10.1111/lapo.12252 · Cited by 17 · see citing works
Integrating Artificial Intelligence into Causal Research in Epidemiology
Matthay, E. C., Neill, D. B., Titus, A. R., Desai, S. M., Troxel, A. B., Cerdá, M., Diaz, I. M., Santacatterina, M., & Thorpe, L. E. (2025) · Current Epidemiology Reports · doi:10.1007/s40471-025-00359-5 · Cited by 18 · see citing works
“For example, synthetic control methods create artificial control groups by taking weighted averages of the outcomes in untreated units, with the weights selected algorithmically by minimizing differences in confounder values between the treated and synthetic control units [113, 114].”
Learning lessons from over-crediting to ensure additionality in forest carbon credits
Swinfield, T., Williams, A. M., Coomes, D. A., Dales, M., Ferris, P., Guizar‐Coutiño, A., Hartup, J., Holland, J., Jaffer, S., Jones, J. P. G., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71552-3 · Cited by 17 · see citing works
“Quasi-experimental methods are advancing all the time and methods exist which combine features of difference-in-differences and synthetic control, such as interactive fi xed-effects, augmented synthetic controls and synthetic difference-in-differences 49 – 51 .”
Connealy, N. T., Piza, E. L., Arietti, R. A., Mohler, G., & Carter, J. G. (2024) · Journal of Experimental Criminology · doi:10.1007/s11292-024-09617-w · Cited by 16 · see citing works
“…staggered adoption, including synthetic difference-in-difference (Arkhangelsky et al., 2021), partially pooled staggered synthetic control methods (Ben-Michael et al., 2021, 2022), and group time averages for two-way fixed effects for difference-in-difference with multiple time periods (Callaway…”
The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy
Cerqueti, R., Coppier, R., Girardi, A., & Ventura, M. (2021) · Econometrics Journal · doi:10.1093/ectj/utab027 · Cited by 16 · see citing works
“…correction (Powel, 2018; Arkhangelesky et al., 2019; Chernozhukov et al., 2019b). consequently, the effectiveness of non-pharmaceutical interventions (see, e.g., Bonacini et al., 2020; Cameletti, 2020; Eckardt et al., 2020; Lolli et al., 2020; Palladino et al., 2020, Peracchi and Terlizzese, 2020).”
BackgroundThe effect of doubling the minimum wage on employment and earnings in Mexico
Campos-Vázquez, R. M., & Esquivel, G. (2021) · Economics Letters · doi:10.1016/j.econlet.2021.110124 · Cited by 16 · see citing works
Estimating the effects of a California gun control program with multitask Gaussian processes
Ben‐Michael, E., Arbour, D. T., Feller, A., Franks, A. M., & Raphael, S. P. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1654 · Cited by 17 · see citing works
“There is a large literature on extensions and modifications to SCM; see Doudchenko and Imbens (2016) and Abadie (2019).”
MethodResultInfluentialSelf-citationThe effect of international sanctions on the size of the middle class in Iran
Farzanegan, M. R., & Habibi, N. (2025) · European Journal of Political Economy · doi:10.1016/j.ejpoleco.2025.102749 · Cited by 20 · see citing works
The Impact of the German Fuel Discount on Prices at the Petrol Pump
Seiler, V., & Stoeckmann, N. (2023) · German Economic Review · doi:10.1515/ger-2022-0108 · Cited by 15 · see citing works
Deregulation of public civilian gun carrying and violent crimes: A longitudinal analysis 1981–2019
Doucette, M. L., Crifasi, C. K., McCourt, A. D., Ward, J. A., Fix, R. L., & Webster, D. W. (2023) · Criminology & Public Policy · doi:10.1111/1745-9133.12638 · Cited by 15 · see citing works
Target Trial Emulation for Evaluating Health Policy
Seewald, N. J., McGinty, E. E., & Stuart, E. A. (2024) · Annals of Internal Medicine · doi:10.7326/m23-2440 · Cited by 15 · see citing works
Casey, J. A., Tartof, S. Y., Davis, M. F., Nachman, K. E., Price, L. B., Liu, C. H., Yu, K. C., Gupta, V., Innes, G. K., Tseng, H. F., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp11221 · Cited by 15 · see citing works
“…a regularized outcome regression model (using ridge regression with the penalty parameter chosen via cross-validation) to improve pre-policy fi t. 26 In this analysis, control states are weighted — forming the synthetic control — such that their weighted average outcomes and covariates in the…”
InfluentialDoes the Swiss Debt Brake Induce Sound Federal Finances? A Synthetic Control Analysis
Pfeil, C. F., & Feld, L. P. (2023) · Public Finance Review · doi:10.1177/10911421231191566 · Cited by 14 · see citing works
“For further analyses, see Firpo and Possebom (2018) as well as Ben-Michael, Feller, and Rothstein (2021).”
The short‐term macroeconomic impact of populism: A case study of Poland
Brzeziński, M., & Sałach, K. (2024) · Economics and Politics · doi:10.1111/ecpo.12273 · Cited by 14 · see citing works
Nurturing nature: The role of green finance in reviving urban biodiversity
Gao, Z., Zhao, Y., Li, L., & Hao, Y. (2025) · The Quarterly Review of Economics and Finance · doi:10.1016/j.qref.2025.102005 · Cited by 14 · see citing works
Hospital quality risk standardization via approximate balancing weights
Keele, L. J., Ben‐Michael, E., Feller, A., Kelz, R. R., & Miratrix, L. W. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1629 · Cited by 13 · see citing works
“Here, we choose to penalize the sum of the squared weights,∑ Zi=j γ̂2i , though other penalties are possible; see Ben-Michael et al. (2021).”
BackgroundMethodInfluentialSelf-citationThe Cross of Gold: Brazilian Treasure and the Decline of Portugal
Kedrosky, D., & Palma, N. P. G. (2025) · The Journal of Economic History · doi:10.1017/s0022050725100764 · Cited by 13 · see citing works
Kesari, A. (2022) · Journal of Empirical Legal Studies · doi:10.1111/jels.12331 · Cited by 13 · see citing works
How Counterfactual Fairness Modelling in Algorithms Can Promote Ethical Decision-Making
Schutter, L. D., & Cremer, D. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2247624 · Cited by 13 · see citing works
“Specifically, ML models can create synthetic con-trol conditions (i.e., a counterfactual scenario absent of a particular cause) that can be used to estimate causal effects (Ben-Michael et al., 2021).”
MethodTakahata, K., Suetsugu, H., Fukaya, K., & Shirota, S. (2024) · Environmental Data Science · doi:10.1017/eds.2024.5 · Cited by 13 · see citing works
Do Foreign Buyer Taxes Affect House Prices?
Hartley, J., Ma, L., Wächter, S. M., & Zevelev, A. A. (2024) · Journal of Real Estate Research · doi:10.1080/08965803.2024.2376955 · Cited by 13 · see citing works
The Long-Run Effects of R&D Place-Based Policies: Evidence from Russian Science Cities
Schweiger, H., Stepanov, A., & Zacchia, P. (2022) · American Economic Journal Economic Policy · doi:10.1257/pol.20200289 · Cited by 13 · see citing works
Introduction to the Special Section on Synthetic Control Methods
Abadie, A., & Cattaneo, M. D. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.2002600 · Cited by 12 · see citing works
“Uncertainty Quantification/Inference: Cattaneo et al. (2021), Chernozhukov et al. (2021), Shaikh and Toulis (2021) Inference with synthetic control estimators is often carried out by permutation techniques after adopting a benchmark distribution for the assignment mechanism (see, e.g., Abadie et al., 2010; Firpo and P…”
BackgroundMethodTownsend, T., Hamilton, L., Rivera‐Aguirre, A., Davis, C. S., Pamplin, J. R., Kline, D., Rudolph, K. E., & Cerdá, M. (2022) · American Journal of Epidemiology · doi:10.1093/aje/kwac122 · Cited by 12 · see citing works
- 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 · 2020: 14 · 2019: 5 · 2018: 1
- 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 › Decision Sciences › Management Science and Operations Research › 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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