References#
Abadie, Alberto. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature, vol. 59, no. 2, 2021, pp. 391–425. https://doi.org/10.1257/jel.20191450.
Li, Kathleen T., and Christophe Van den Bulte. “Augmented Difference-in-Differences.” Marketing Science, vol. 42, no. 4, 2023, pp. 746–767. https://doi.org/10.1287/mksc.2022.1406.
Li, Kathleen T., and David R. Bell. “Estimation of Average Treatment Effects with Panel Data: Asymptotic Theory and Implementation.” J. Econom., vol. 197, no. 1, March 2017, pp. 65–75. https://doi.org/10.1016/j.jeconom.2016.01.011.
Amjad, Muhammad, Devavrat Shah, and Dennis Shen. “Robust Synthetic Control.” Journal of Machine Learning Research, vol. 19, no. 1, 2018, pp. 802–852.
Agarwal, Anish, Devavrat Shah, Dennis Shen, and Dogyoon Song. “On Robustness of Principal Component Regression.” Journal of the American Statistical Association, vol. 116, no. 536, 2021, pp. 1731–1745. doi:10.1080/01621459.2021.1928513.
Rho, Saeyoung, Tang, Andrew, Bergam, Noah, Cummings, Rachel, and Misra, Vishal. “ClusterSC: Advancing Synthetic Control with Donor Selection.” Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 258, 2025. arXiv:2503.21629.
Bai, Jushan and Serena Ng (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70 (1), 191–222.
Bayani, Mani. “Robust PCA Synthetic Control.” Working paper, arXiv, 2021. doi:10.48550/ARXIV.2108.12542.
de Chaisemartin, Clément and D’Haultfoeuille, Xavier. “Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey.” The Econometrics Journal, 26(3): C1-C30, 2022. DOI: https://doi.org/10.1093/ectj/utac017
Luis Costa, Vivek F. Farias, Patricio Foncea, Jingyuan (Donna) Gan, Ayush Garg, Ivo Rosa Montenegro, Kumarjit Pathak, Tianyi Peng, Dusan Popovic. “Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure.” INFORMS Journal on Applied Analytics, vol. 53, no. 5, 2023, pp. 336–349.
Sourav Chatterjee. “Matrix estimation by Universal Singular Value Thresholding.” The Annals of Statistics, Ann. Statist. 43(1), 177-214, (February 2015).
David Donoho, Matan Gavish, Elad Romanov. “ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise.” The Annals of Statistics, Ann. Statist. 51(1), 122-148, (February 2023).
Hsiao, Cheng, H. Steve Ching, and Shui Ki Wan. “A Panel Data Approach for Program Evaluation: Measuring the Benefits of Political and Economic Integration of Hong Kong with Mainland China.” Journal of Applied Econometrics 27, no. 5 (2012): 705-40. DOI: 10.1002/jae.1230.
Caroline Fohlin and Zhikun Lu. “How Contagious Was the Panic of 1907? New Evidence from Trust Company Stocks.” AEA Papers and Proceedings, vol. 111, pp. 514-519, 2021. DOI: `10.1257/pandp.20211097`_.
Li, Kathleen T. “Frontiers: A Simple Forward Difference-in-Differences Method.” Marketing Science, vol. 43, no. 2, 2024, pp. 239–468. doi:10.1287/mksc.2022.0212.
Jizhou Liu, Eric Tchetgen Tchetgen, and Carlos Varjão. “Proximal Causal Inference for Synthetic Control with Surrogates.” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, vol. 238, pp. 730-738, PMLR, May 2024. Available at: `https://proceedings.mlr.press/v238/liu24a.html`_.
Li, K. T., & Shankar, V. (2023). A Two-Step Synthetic Control Approach for Estimating Causal Effects of Marketing Events. *Management Science, 70*(6), 3734-3747. https://doi.org/10.1287/mnsc.2023.4878
Roth, Jonathan, Sant’Anna, Pedro H.C., Bilinski, Alyssa, and Poe, John. “What’s trending in difference-in-differences? A synthesis of the recent econometrics literature.” Journal of Econometrics, 235(2): 2218-2244, 2023. DOI: https://doi.org/10.1016/j.jeconom.2023.03.008
Zhentao Shi and Yishu Wang. “L2-relaxation for Economic Prediction.” November 2024. doi:10.13140/RG.2.2.11670.97609.
Shi, Zhentao and Huang, Jingyi. “Forward-selected panel data approach for program evaluation.” Journal of Econometrics, 234(2): 512-535, 2023. DOI: https://doi.org/10.1016/j.jeconom.2021.04.009
Clarke, Damian, Pailañir, Daniel, Athey, Susan, and Imbens, Guido. “On Synthetic Difference-in-Differences and Related Estimation Methods in Stata.” Working Paper. Available at: https://doi.org/10.48550/arXiv.2301.11859
Wing, Coady, Freedman, Seth M., and Hollingsworth, Alex. “Stacked Difference-in-Differences.” National Bureau of Economic Research, Working Paper Series, 32054, January 2024. DOI: https://doi.org/10.3386/w32054
Arkhangelsky, Dmitry, Athey, Susan, Hirshberg, David A., Imbens, Guido W., and Wager, Stefan. “Synthetic Difference-in-Differences.” American Economic Review, 111(12): 4088–4118, 2021. URL: https://doi.org/10.1257/aer.20190159
Abadie, Alberto and L’Hour, Jérémy. “A Penalized Synthetic Control Estimator for Disaggregated Data.” Journal of the American Statistical Association, 116(536): 1817–1834, 2021. DOI: https://doi.org/10.1080/01621459.2021.1971535
Li, Kathleen T. and Shankar, Venkatesh. “A Two-Step Synthetic Control Approach for Estimating Causal Effects of Marketing Events.” Management Science, 70(6): 3734-3747, 2024. DOI: https://doi.org/10.1287/mnsc.2023.4878
Li, Kathleen T. and Sonnier, Garrett P. “Statistical Inference for the Factor Model Approach to Estimate Causal Effects in Quasi-Experimental Settings.” Journal of Marketing Research, 60(3): 449-472, 2023. DOI: https://doi.org/10.1177/00222437221137533
Becker, Martin and Klößner, Stefan. “Fast and reliable computation of generalized synthetic controls.” Econometrics and Statistics, 5: 1-19, 2018. DOI: https://doi.org/10.1016/j.ecosta.2017.08.002
Albalate, Daniel, Bel, Germà, and Mazaira-Font, Ferran A. “Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness.” SERIEs, 12(4): 549-584, 2021. Publisher: Springer
Li, Kathleen T. and Sonnier, Garrett P. “Statistical inference for the factor model approach to estimate causal effects in quasi-experimental settings.” Journal of Marketing Research, 60(3): 449–472, 2023. Publisher: SAGE Publications
Robbins, Michael W., Saunders, Jessica, and Kilmer, Beau. “A Framework for Synthetic Control Methods With High-Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Intervention.” Journal of the American Statistical Association, 112(517): 109-126, 2017. DOI: https://doi.org/10.1080/01621459.2016.1213634
Abadie, Alberto, Diamond, Alexis, and Hainmueller, Jens. “Comparative Politics and the Synthetic Control Method.” American Journal of Political Science, 59(2): 495-510, 2015. DOI: https://doi.org/10.1111/ajps.12116
Malo, Pekka, Eskelinen, Juha, Zhou, Xun, and Kuosmanen, Timo. “Computing synthetic controls using bilevel optimization.” Computational Economics, 2023. DOI: https://doi.org/10.1007/s10614-023-10471-7
Vives-i-Bastida, Jaume. “Predictor Selection for Synthetic Controls.” Working Paper, 2022. URL: https://arxiv.org/abs/2203.11576
Imbens, Guido W. “Causal Inference in the Social Sciences.” Annual Review of Statistics and Its Application, 11: 123-152, 2024. DOI: https://doi.org/10.1146/annurev-statistics-033121-114601
Dague, Laura and Lahey, Joanna N. “Causal Inference Methods: Lessons from Applied Microeconomics.” Journal of Public Administration Research and Theory, 29(3): 511-529, 2018. DOI: https://doi.org/10.1093/jopart/muy067
Shi, Xu, Kendrick Li, Wang Miao, Mengtong Hu, and Eric Tchetgen Tchetgen. “Theory for Identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework.” 2023. https://doi.org/10.48550/arXiv.2108.13935
Shi, Xu, Wang Miao, Jennifer C. Nelson, and Eric J. Tchetgen Tchetgen. “Multiply Robust Causal Inference with Double-Negative Control Adjustment for Categorical Unmeasured Confounding.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82, no. 2 (2020): 521-540. DOI: https://doi.org/10.1111/rssb.12361
Park, Chan, and Eric J. Tchetgen Tchetgen. “Single Proxy Synthetic Control.” Journal of Causal Inference 13, no. 1 (2025): 20230079. DOI: https://doi.org/10.1515/jci-2023-0079
Qiu, Hongxiang, Xu Shi, Wang Miao, Edgar Dobriban, and Eric Tchetgen Tchetgen. “Doubly Robust Proximal Synthetic Controls.” Biometrics 80, no. 2 (2024): ujae055. DOI: https://doi.org/10.1093/biomtc/ujae055
Abadie, Alberto and Gardeazabal, Javier. “The Economic Costs of Conflict: A Case Study of the Basque Country.” American Economic Review, 93(1): 113-132, 2003. DOI: https://doi.org/10.1257/000282803321455188
Kinn, Daniel. “Synthetic Control Methods and Big Data.” arXiv Working Paper, 1803.00096, 2018. DOI: https://doi.org/10.48550/arXiv.1803.00096
Wiltshire, Justin C. “allsynth: Synthetic Control Bias-Correction Utilities for Stata.” Working Paper, 2021.
Greathouse, Jared. “Scul: Regularized Synthetic Controls in Stata.” Georgia State University, 08, 2022. DOI: https://doi.org/10.2139/ssrn.4196189
Abadie, Alberto and Zhao, Jinglong. “Synthetic Controls for Experimental Design.” arXiv Working Paper, 2108.02196, 2024. DOI: https://arxiv.org/abs/2108.02196
Ferman, Bruno, Pinto, Cristine, and Possebom, Vitor. “Cherry Picking with Synthetic Controls.” Journal of Policy Analysis and Management, 39(2): 510-532, 2020. DOI: https://doi.org/10.1002/pam.22206
Viviano, Davide and Bradic, Jelena. “Synthetic Learner: Model-free inference on treatments over time.” Journal of Econometrics, 234(2): 691-713, 2023. DOI: https://doi.org/10.1016/j.jeconom.
Yan, Guanpeng and Chen, Qiang. “rcm: A command for the regression control method.” The Stata Journal, 22(4): 842-883, 2022. URL: https://doi.org/10.1177/1536867X221140960
Abadie, Alberto, Diamond, Alexis, and Hainmueller, Jens. “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association, 105(490): 493-505, 2010. URL: https://doi.org/10.1198/jasa.2009.ap08746
Cerulli, Giovanni. “Optimal initial donor selection for the synthetic control method.” Economics Letters, 244: 111976, 2024. DOI: https://doi.org/10.1016/j.econlet.2024.111976
Liu, Licheng, Wang, Ye, and Xu, Yiqing. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” American Journal of Political Science, 68(1): 160-176, 2024. URL: https://doi.org/10.1111/ajps.12723
Yan, Guanpeng and Chen, Qiang. “synth2: Synthetic control method with placebo tests, robustness test, and visualization.” The Stata Journal, 23(3): 597-624, 2023. URL: https://doi.org/10.1177/1536867X231195278
Xu, Yiqing. “Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models.” Political Analysis 25, no. 1 (2017): 57–76. https://doi.org/10.1017/pan.2016.2.
Athey, Susan, Bayati, Mohsen, Doudchenko, Nikolay, Imbens, Guido, and Khosravi, Khashayar. “Matrix Completion Methods for Causal Panel Data Models.” Journal of the American Statistical Association, 116(536): 1716-1730, 2021. DOI: https://doi.org/10.1080/01621459.2021.1891924
Mazumder, Rahul, Hastie, Trevor, and Tibshirani, Robert. “Spectral Regularization Algorithms for Learning Large Incomplete Matrices.” Journal of Machine Learning Research, 11: 2287-2322, 2010.
Shen, Zikai, Song, Xinkun, and Abadie, Alberto. “Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data.” arXiv Working Paper, 2510.22828, 2025. URL: https://arxiv.org/abs/2510.22828
Cattaneo, Matias D., Feng, Yingjie, Palomba, Filippo, and Titiunik, Rocío. “Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption.” Review of Economics and Statistics (forthcoming); arXiv Working Paper, 2210.05026, 2025. URL: https://arxiv.org/abs/2210.05026
Cao, Jianfei, Lu, Shirley, and Wu, Hang. “Synthetic Control Inference for Staggered Adoption.” The Econometrics Journal (forthcoming), 2026. URL: https://doi.org/10.1093/ectj/utag015
Agarwal, Anish, Choi, Jungjun, and Yuan, Ming. “Robust Matrix Estimation with Side Information.” arXiv Working Paper, 2603.24833, 2026. URL: https://arxiv.org/abs/2603.24833
O’Riordan, Michael, and Gilligan-Lee, Ciarán M. “Spillover Detection for Donor Selection in Synthetic Control Models.” Journal of Causal Inference 13(1):20240036, 2025. URL: https://doi.org/10.1515/jci-2024-0036
Doudchenko, Nick, Khosravi, Khashayar, Pouget-Abadie, Jean, Lahaie, Sebastien, Lubin, Miles, Mirrokni, Vahab, Spiess, Jann, and Imbens, Guido. “Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls.” Advances in Neural Information Processing Systems (NeurIPS), 2021. arXiv:2112.00278. URL: https://arxiv.org/abs/2112.00278
Lu, Yiping, Li, Jiajin, Ying, Lexing, and Blanchet, Jose. “Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls.” arXiv Working Paper, 2211.15241, 2022. URL: https://arxiv.org/abs/2211.15241
Liu, Ziyi, and Xu, Yiqing. “The Harmonic Synthetic Control Method.” Working Paper, 2026.
Ben-Michael, Eli, Feller, Avi, and Rothstein, Jesse. “Synthetic Controls with Staggered Adoption.” Journal of the Royal Statistical Society: Series B, 84(2): 351-381, 2022. DOI: https://doi.org/10.1111/rssb.12448
Gulek, Atilla, and Vives-i-Bastida, Jaume. “Synthetic IV Estimation in Panels.” Working Paper, 2024.
Rho, Saeyoung, Illick, Cyrus, Narasipura, Samhitha, Abadie, Alberto, Hsu, Daniel, and Misra, Vishal. “Time-Aware Synthetic Control.” arXiv Preprint, 2601.03099, 2026. URL: https://arxiv.org/abs/2601.03099
Autor, David H., Dorn, David, and Hanson, Gordon H. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.” American Economic Review, 103(6): 2121-2168, 2013. DOI: https://doi.org/10.1257/aer.103.6.2121
Wang, Xing, and Ye. “L-infinity-norm Synthetic Control.” Working Paper. (Title, journal, year, and DOI to be completed.)
Liao, Shi, and Zheng. “Synthetic Control Relaxation.” Working Paper. (Title, journal, year, and DOI to be completed.)
Li. “Synthetic Control Methods.” Working Paper. (Authors, title, journal, year, and DOI to be completed.)