Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis
Open accessAbstract
In a seminal paper Abadie et al (2010) develop the synthetic control procedure for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed for all units.The method constructs a set of weights such that covariates and pre-treatment outcomes of the treated unit are approximately matched by a weighted average of control units.The weights are restricted to be nonnegative and sum to one, which allows the procedure to obtain the weights even when the number of lagged outcomes is modest relative to the number of control units, a setting that is not uncommon in applications.In the current paper we propose a more general class of synthetic control estimators that allows researchers to relax some of the restrictions in the ADH method.We allow the weights to be negative, do not necessarily restrict the sum of the weights, and allow for a permanent additive difference between the treated unit and the controls, similar to difference-in-difference procedures.The weights directly minimize the distance between the lagged outcomes for the treated and the control units, using regularization methods to deal with a potentially large number of possible control units.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent work has begun to try to combine insights from the difference-in-differences and synthetic control literatures.
“Recent work has begun to try to combine insights from the two literatures (e.g., Arkhangelsky, Athey, Hirshberg, Imbens and Wager, 2021; Ben-Michael, Feller and Rothstein, 2021, 2022; Doudchenko and Imbens, 2016).”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Doudchenko and Imbens, 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641 · see citing works
The economic costs of conflict: a case study of the Basque Country
Abadie, A., & Gardeazabal, J. (2003) · American Economic Review · doi:10.1257/000282803321455188 · Cited by 4,517 · see citing works
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210 · see citing works
Self-citationLarge sample properties of matching estimators for average treatment effects
Abadie, A., & Imbens, G. W. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00655.x · Cited by 2,908 · see citing works
Self-citationStatistical Learning with Sparsity: The Lasso and Generalizations
Hastie, T., Tibshirani, R., & Wainwright, M. (2015) · International Statistical Review · doi:10.1111/insr.12167 · Cited by 2,489 · see citing works
Comparative Politics and the Synthetic Control Method
Abadie, A., Diamond, A., & Hainmueller, J. (2014) · American Journal of Political Science · doi:10.1111/ajps.12116 · Cited by 2,374 · see citing works
Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania
Card, D., & Krueger, A. B. (1993) · National Bureau of Economic Research · doi:10.3386/w4509 · Cited by 2,072 · see citing works
Empirical Strategies in Labor Economics
Angrist, J. D., & Krueger, A. B. (1999) · Handbook of Labor Economics · doi:10.1016/s1573-4463(99)03004-7 · Cited by 1,628 · see citing works
Covariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2013) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12027 · Cited by 1,330 · see citing works
(1997) · Cited by 1,193 · see citing works
Diamond, A., & Sekhon, J. S. (2012) · The Review of Economics and Statistics · doi:10.1162/rest_a_00318 · Cited by 1,066 · see citing works
Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs
Ashenfelter, O., & Card, D. (1985) · The Review of Economics and Statistics · doi:10.2307/1924810 · Cited by 1,023 · see citing works
Inferring causal impact using Bayesian structural time-series models
Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015) · The Annals of Applied Statistics · doi:10.1214/14-aoas788 · Cited by 953 · see citing works
Abadie, A., Diamond, A., & Hainmueller, J. (2007) · National Bureau of Economic Research · doi:10.3386/t0335 · Cited by 830 · see citing works
A Penalized Synthetic Control Estimator for Disaggregated Data
Abadie, A., & L’Hour, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1971535 · Cited by 240 · see citing works
Inverse probability tilting for moment condition models with missing data
Graham, B. S., De Xavier Pinto, C. C., & Egel, D. (2012) · The Review of Economic Studies · doi:10.1093/restud/rdr047 · Cited by 174 · see citing works
Identification and Inference in Nonlinear Difference-in-Differences ModelsPreprint
Athey, S. C., & Imbens, G. W. (2002) · SSRN Electronic Journal · doi:10.2139/ssrn.311920 · Cited by 143 · see citing works
Self-citationMostly Harmless Econometrics: An Empiricist’s Companion
Harris, D., & Skeels, C. L. (2011) · Economic Record · doi:10.1111/j.1475-4932.2011.00742.x · Cited by 120 · see citing works
Workers' Compensation and Injury Duration: Evidence from a Natural Experiment
Meyer, B., Viscusi, W. K., & Durbin, D. L. (1990) · National Bureau of Economic Research · doi:10.3386/w3494 · Cited by 106 · see citing works
Revisiting the synthetic control estimator
Ferman, B., & Pinto, C. C. D. X. (2016) · Americanae (AECID Library) · Cited by 66 · see citing works
Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)
Graham, B. S., Pinto, C. C. D. X., & Egel, D. (2015) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2015.1038544 · Cited by 59 · see citing works
Peri, G., & Yasenov, V. I. (2015) · National Bureau of Economic Research · doi:10.3386/w21801 · Cited by 55 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- Michihito Ando and F Sävje. Hypothesis testing with the synthetic control method. In European Economic Association and Econometric Society Meeting, pages 1-35, 2013.
- Joshua Angrist and Steve Pischke. Mostly Harmless Econometrics: An Empiricists' Companion. Princeton University Press, 2008.
- Susan Athey, Guido Imbens, and Stefan Wager. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv preprint arXiv:1604.07125, 2016. · arXiv
- David Card. The impact of the mariel boatlift on the miami labor market. Industrial and Labor Relation, 43(2):245-257, 1990.
- Jens Hainmueller. Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1):25-46, 2012.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York: Springer, 2009.
- Paul W Holland. Statistics and causal inference. Journal of the American Statistical Association, 81(396):945-970, 1986.
- Guido W Imbens and Donald B Rubin. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press, 2015.
- Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5):688-701, 1974.
- Donald B Rubin. Matched sampling for causal effects. Cambridge University Press, 2006.
- Vadim von Brzeski, Matt Taddy, and David Draper. Causal inference in repeated observational studies: A case study of ebay product releases. arXiv preprint arXiv:1509.03940, 2015. · arXiv
Cited by
Sorted by citations
What's trending in difference-in-differences? A synthesis of the recent econometrics literature
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2023.03.008 · Cited by 2,027 · see citing works
Designing Difference in Difference Studies: Best Practices for Public Health Policy Research
Wing, C., Simon, K., & Bello-Gomez, R. A. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-013507 · Cited by 1,470 · see citing works
Synthetic Difference-in-Differences
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021) · American Economic Review · doi:10.1257/aer.20190159 · Cited by 1,518 · see citing works
Self-citationUsing Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
Abadie, A. (2021) · Journal of Economic Literature · doi:10.1257/jel.20191450 · Cited by 1,486 · see citing works
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163 · see citing works
Self-citationMatrix Completion Methods for Causal Panel Data Models
Athey, S., Bayati, M., Doudchenko, N., Imbens, G., & Khosravi, K. (2018) · doi:10.3386/w25132 · Cited by 497 · see citing works
Self-citationThe Augmented Synthetic Control Method
Ben-Michael, E., Feller, A., & Rothstein, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1929245 · Cited by 462 · see citing works
Econometric Methods for Program Evaluation
Abadie, A., & Cattaneo, M. D. (2018) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053402 · Cited by 434 · see citing works
Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data
Imai, K., Kim, I. S., & Wang, E. H. (2023) · American Journal of Political Science · doi:10.1111/ajps.12685 · Cited by 349 · see citing works
Martin-Shields, C. P., & Stojetz, W. (2019) · World Development · doi:10.1016/j.worlddev.2018.07.011 · Cited by 302 · see citing works
Cherry Picking with Synthetic Controls
Ferman, B., Pinto, C. C. D. X., & Possebom, V. (2020) · Journal of Policy Analysis and Management · doi:10.1002/pam.22206 · Cited by 244 · see citing works
A Penalized Synthetic Control Estimator for Disaggregated Data
Abadie, A., & L’Hour, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1971535 · Cited by 240 · see citing works
Synthetic controls with imperfect pretreatment fit
Ferman, B., & Pinto, C. C. D. X. (2021) · Quantitative Economics · doi:10.3982/qe1596 · Cited by 213 · see citing works
Conducting Research in Marketing with Quasi-Experiments
Avi Goldfarb, B., Tucker, C. E., & Wang, Y. (2022) · Journal of Marketing · doi:10.1177/00222429221082977 · Cited by 191 · see citing works
An 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
Kaul, A., Klößner, S., Pfeifer, G., & Schieler, M. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2021.1930012 · Cited by 276 · see citing works
Agricultural Productivity and Forest Conservation: Evidence from the Brazilian Amazon
Koch, N., zu Ermgassen, E. K. H. J., Wehkamp, J., de Filho, F. J. B. O., & Schwerhoff, G. (2017) · American Journal of Agricultural Economics · doi:10.1093/ajae/aay110 · Cited by 136 · see citing works
Regulating Privacy Online: An Economic Evaluation of the GDPR
Goldberg, S. G., Johnson, G. A., & Shriver, S. K. (2024) · American Economic Journal Economic Policy · doi:10.1257/pol.20210309 · Cited by 130 · see citing works
On the role of covariates in the synthetic control method
Botosaru, I., & Ferman, B. (2019) · Econometrics Journal · doi:10.1093/ectj/utz001 · Cited by 128 · see citing works
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
Bai, J., & Ng, S. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1967163 · Cited by 123 · see citing works
Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods
Li, K. T. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1686986 · Cited by 110 · see citing works
Cho, S.-W. S. (2020) · Econometrics Journal · doi:10.1093/ectj/utaa025 · Cited by 102 · see citing works
Did the ACA Medicaid expansion save lives?
Borgschulte, M., & Vogler, J. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2020.102333 · Cited by 103 · see citing works
On synthetic difference-in-differences and related estimation methods in Stata
Clarke, D., Pailañir, D., Athey, S. C., & Imbens, G. W. (2024) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x241297914 · Cited by 98 · see citing works
Self-citationSynthetic Difference-in-Differences EstimationPreprint
Clarke, D., Pailañir, D., Athey, S. C., & Imbens, G. W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4346540 · Cited by 98 · see citing works
Self-citationSynthetic Control and Inference
Hahn, J., & Shi, R. (2017) · Econometrics · doi:10.3390/econometrics5040052 · Cited by 92 · see citing works
Synthetic Difference In Differences
Arkhangelsky, D., Athey, S. C., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2019) · National Bureau of Economic Research · doi:10.3386/w25532 · Cited by 92 · see citing works
Self-citationJones, D., & Marinescu, I. (2018) · National Bureau of Economic Research · doi:10.3386/w24312 · Cited by 86 · see citing works
Using machine learning to assess the livelihood impact of electricity access
Ratledge, N., Cadamuro, G., Cuesta, B. D. L., Stigler, M., & Burke, M. (2022) · Nature · doi:10.1038/s41586-022-05322-8 · Cited by 84 · see citing works
Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method
Xin, M., Shalaby, A. S., Feng, S., & Zhao, H. (2021) · Transport Policy · doi:10.1016/j.tranpol.2021.07.006 · Cited by 79 · see citing works
Identifying the effects of sanctions on the Iranian economy using newspaper coverage
Laudati, D., & Pesaran, M. H. (2022) · Journal of Applied Econometrics · doi:10.1002/jae.2947 · Cited by 79 · see citing works
Crime is Terribly Revealing: Information Technology and Police Productivity
Mastrobuoni, G. (2017) · The Review of Economic Studies · doi:10.1093/restud/rdaa009 · Cited by 76 · see citing works
Are We #Stayinghome to Flatten the Curve?
Sears, J., Villas‐Boas, J. M., Villas-Boas, V., & Villas‐Boas, S. B. (2022) · American Journal of Health Economics · doi:10.1101/2020.05.23.20111211 · Cited by 76 · see citing works
The impact of cannabis legalization in Uruguay on adolescent cannabis use
Laqueur, H. S., Rivera‐Aguirre, A., Shev, A. B., Castillo‐Carniglia, Á., Rudolph, K. E., Ramirez, J., Martins, S. S., & Cerdá, M. (2020) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2020.102748 · Cited by 75 · see citing works
Synthetic Control Estimator: A Tool for Comparative Case Studies in Economic History
Gilchrist, D. J., Emery, T., Garoupa, N. M., & Spruk, R. (2020) · Journal of Economic Surveys · doi:10.1111/joes.12493 · Cited by 75 · see citing works
Heterogeneous effects of China's low-carbon city pilots policy.
Lyu, J., Liu, T., Cai, B.-F., Qi, Y.-P., & Zhang, X. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.118329 · Cited by 62 · see citing works
Xiong, R., & Pelger, M. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.005 · Cited by 64 · see citing works
Hazlett, C. J., & Xu, Y. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3214231 · Cited by 58 · see citing works
Buggs, S. A., Webster, D. W., & Crifasi, C. K. (2022) · Injury Prevention · doi:10.1136/injuryprev-2020-044056 · Cited by 57 · see citing works
Bayesian Synthetic Control Methods
Kim, S., Lee, C. C., & Gupta, S. (2020) · Journal of Marketing Research · doi:10.1177/0022243720936230 · Cited by 53 · see citing works
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsPreprint
Chernozhukov, V., Wüthrich, K., & Zhu, Y. (2021) · RePEc: Research Papers in Economics · doi:10.6084/m9.figshare.14473870 · Cited by 55 · see citing works
Jones, D., & Marinescu, I. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3118343 · Cited by 55 · see citing works
Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation
Kellogg, M., Mogstad, M., Pouliot, G., & Torgovitsky, A. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1979562 · Cited by 54 · see citing works
Hollingsworth, A., & Wing, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3592088 · Cited by 53 · see citing works
Saggu, A., & Ante, L. (2023) · Finance research letters · doi:10.1016/j.frl.2023.103993 · Cited by 51 · see citing works
A Bayesian Alternative to Synthetic Control for Comparative Case Studies
Pang, X., Liu, L., & Xu, Y. (2022) · Political Analysis · doi:10.1017/pan.2021.22 · Cited by 57 · see citing works
Causal models for longitudinal and panel data: a survey
Arkhangelsky, D., & Imbens, G. (2024) · The Econometrics Journal · doi:10.1093/ectj/utae014 · Cited by 50 · see citing works
Self-citationBrazil’s Missing Infants: Zika Risk Changes Reproductive Behavior
Rangel, M. A., Nobles, J. E., & Hamoudi, A. (2020) · Demography · doi:10.1007/s13524-020-00900-9 · Cited by 49 · see citing works
Machine Learning Methods Economists Should Know AboutPreprint
Athey, S. C., & Imbens, G. W. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1903.10075 · Cited by 46 · see citing works
Self-citationMeng, Y., Yu, J., Yu, Y., & Ren, Y. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123394 · Cited by 46 · see citing works
Powell, D. (2022) · Journal of Business & Economic Statistics · doi:10.1080/07350015.2021.1927743 · Cited by 43 · see citing works
The Effect of Sell-by Dates on Purchase Volume and Food Waste
Yu, Y., & Jaenicke, E. C. (2018) · Food Policy · doi:10.1016/j.foodpol.2020.101879 · Cited by 43 · see citing works
Interaction effects of market-based and incentive-driven low-carbon policies on carbon emissions
Jia, Z., & Wen, S. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.107776 · Cited by 43 · see citing works
Fick, S. E., Nauman, T., Brungard, C., & Duniway, M. C. (2020) · Ecological Applications · doi:10.1002/eap.2264 · Cited by 41 · see citing works
Goldberg, S. G., Johnson, G. A., & Shriver, S. K. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3421731 · Cited by 40 · see citing works
Alrababa’h, A., Marble, W., Mousa, S., & Siegel, A. A. (2019) · American Political Science Review · doi:10.31235/osf.io/eq8ca · Cited by 40 · see citing works
Matthay, E. C., Farkas, K., Rudolph, K. E., Zimmerman, S., Barragan, M., Goin, D. E., & Ahern, J. (2019) · American Journal of Public Health · doi:10.2105/ajph.2019.305288 · Cited by 39 · see citing works
Unfunded Mandates and Fiscal Structure: Empirical Evidence from a Synthetic Control Model
Ross, J. M. (2016) · Public Administration Review · doi:10.1111/puar.12867 · Cited by 38 · see citing works
Can your advertising really buy earned impressions? The effect of brand advertising on word of mouth
Lovett, M. J., Peres, R., & Xu, L. (2019) · Quantitative Marketing and Economics · doi:10.1007/s11129-019-09211-9 · Cited by 38 · see citing works
The role of airports in city employment growth, 1950–2010
McGraw, M. J. (2020) · Journal of Urban Economics · doi:10.1016/j.jue.2020.103240 · Cited by 38 · see citing works
Goh, L. S. (2021) · Criminology & Public Policy · doi:10.1111/1745-9133.12536 · Cited by 37 · see citing works
The economic consequences of fiscal rules
Potrafke, N. (2025) · Journal of International Money and Finance · doi:10.1016/j.jimonfin.2025.103286 · Cited by 36 · see citing works
Does Collateral Value Affect Asset Prices? Evidence from a Natural Experiment in Texas
Zevelev, A. A. (2016) · Review of Financial Studies · doi:10.1093/rfs/hhaa117 · Cited by 40 · see citing works
The Cuban revolution and infant mortality: A synthetic control approach
Geloso, V. J., & Pavlik, J. B. (2020) · Explorations in Economic History · doi:10.1016/j.eeh.2020.101376 · Cited by 35 · see citing works
Yılmaz, Ö., Son, Y., Shang, G., & Arslan, H. A. (2024) · Journal of Operations Management · doi:10.1002/joom.1318 · Cited by 34 · 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
A comparison of methods for health policy evaluation with controlled pre‐post designs
O’Neill, S., Kreif, N., Sutton, M., & Grieve, R. (2020) · Health Services Research · doi:10.1111/1475-6773.13274 · Cited by 31 · see citing works
Banning Foreign Pharmacies from Sponsored Search: The Online Consumer Response
Chesnes, M. W., Dai, W., & Jin, G. Z. (2017) · Marketing Science · doi:10.1287/mksc.2017.1058 · Cited by 31 · see citing works
Adbi, A., Chatterjee, C., Drev, M., & Mishra, A. (2018) · Production and Operations Management · doi:10.1111/poms.12948 · Cited by 29 · see citing works
Does Financial Deregulation Boost Top Incomes? Evidence from the Big Bang
Tanndal, J., & Waldenström, D. (2017) · Economica · doi:10.1111/ecca.12247 · Cited by 26 · see citing works
Fiscal and Education Spillovers from Charter School Expansion
Ridley, M., & Terrier, C. (2018) · National Bureau of Economic Research · doi:10.3386/w25070 · Cited by 26 · see citing works
An exact and robust conformal inference method for counterfactual and synthetic controls
Chernozhukov, V., Wüthrich, K., & Zhu, Y. (2018) · doi:10.1920/wp.cem.2017.6217 · Cited by 25 · see citing works
Distributional Synthetic Controls
Gunsilius, F. F. (2023) · Econometrica · doi:10.3982/ecta18260 · 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
Bonander, C. (2017) · Injury Prevention · doi:10.1136/injuryprev-2017-042360 · Cited by 23 · see citing works
When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method
Parast, L. M., Hunt, P., Griffin, B. A., & Powell, D. (2020) · Journal of Causal Inference · doi:10.1515/jci-2020-0013 · Cited by 22 · see citing works
Synthetic Control as Online Linear Regression
Chen, J. (2023) · Econometrica · doi:10.3982/ecta20720 · Cited by 22 · see citing works
Nonlinear asset pricing in Chinese stock market: A deep learning approach
Pan, S., Long, S., Wang, Y., & Xie, Y. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102627 · Cited by 22 · see citing works
On Robustness of Principal Component RegressionPreprint
Agarwal, A., Devavrat Shah, B., Shen, D., & Song, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10920 · Cited by 22 · see citing works
Cai, J., Xin, K. Z., & Zhou, Y. (2021) · Journal of Management Science and Engineering · doi:10.1016/j.jmse.2021.06.004 · Cited by 21 · see citing works
ARCO: An Artificial Counterfactual Approach For High-Dimensional Panel Time-Series DataPreprint
Carvalho, C. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2823687 · Cited by 21 · see citing works
Does the market model provide a good counterfactual for event studies in finance?
Castro-Iragorri, C. (2019) · Financial markets and portfolio management · doi:10.1007/s11408-019-00325-4 · Cited by 20 · see citing works
Immigration enforcement, crime, and demography
Chalfin, A. J., & Deza, M. (2020) · Criminology & Public Policy · doi:10.1111/1745-9133.12498 · Cited by 19 · see citing works
B, J. F. A., Masini, R., & Medeiros, M. C. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.2004895 · Cited by 19 · see citing works
The Intergenerational Effects of Economic Sanctions
Moeeni, S. (2021) · The World Bank Economic Review · doi:10.1093/wber/lhab024 · Cited by 19 · see citing works
Garber, M. D., Flanders, W. D., Watkins, K. E., Lobelo, F., Kramer, M. R., & McCullough, L. E. (2022) · Epidemiology · doi:10.1097/ede.0000000000001483 · Cited by 19 · see citing works
Clarke, 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
Randomization tests in observational studies with staggered adoption of treatment
Shaikh, A. M., & Toulis, P. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1974458 · Cited by 19 · 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
Synthetic InterventionsPreprint
Agarwal, A., Devavrat Shah, B., & Shen, D. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2006.07691 · Cited by 18 · see citing works
Inference for Factor Model Based Average Treatment EffectsPreprint
Li, K. Y. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3112775 · Cited by 18 · see citing works
Perilous Flood Risk AssessmentsPreprint
Weill, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4143914 · Cited by 18 · see citing works
Are IMF rescue packages effective? A synthetic control analysis of macroeconomic crises
Kuruc, K. (2022) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2022.02.002 · Cited by 17 · see citing works
Of the first five US states with food waste bans, Massachusetts alone has reduced landfill waste
Anglou, F. Z., Sanders, R. E., & Stamatopoulos, I. (2024) · Science · doi:10.1126/science.adn4216 · Cited by 17 · see citing works
The emergence of social soft skill needs in the post COVID-19 era
Gnecco, G., Landi, S., & Riccaboni, M. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01659-y · Cited by 16 · see citing works
Focused Deterrence and Program Fidelity: Evaluating the Impact of Detroit Ceasefire
Circo, G. M., Krupa, J. M., McGarrell, E. F., & Biasi, A. D. (2020) · Justice Evaluation Journal · doi:10.1080/24751979.2020.1827938 · 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
Bayesian Method for Causal Inference in Spatially-Correlated Multivariate Time Series
Ning, B., Subhashis Ghosal, B., & Thomas, J. (2018) · Bayesian Analysis · doi:10.1214/18-ba1102 · Cited by 16 · see citing works
- Authors
- Doudchenko, N., & Imbens, G.
- Affiliations
- National Bureau of Economic Research (US)
- Stanford University (US)
- ORCID
- Imbens, Guido
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2016-10
- OpenAlex
- W2540009304
- Cited by
- 410
- References
- 22
- Citations by year
- 2026: 32 · 2025: 54 · 2024: 51 · 2023: 44 · 2022: 44 · 2021: 72 · 2020: 41 · 2019: 34 · 2018: 23 · 2017: 11 · 2016: 3 · 2014: 1
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- National Bureau of Economic Research (submittedVersion)
- arXiv (Cornell University)
- 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, regularization, difference-in-differences, regression discontinuity design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
