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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
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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
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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
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Hollingsworth, Alex, and Wing, Coady. “Tactics for design and inference in synthetic control studies: An applied example using high-dimensional data.” Working paper, 2022. Reference implementation: hollina/scul
Tibshirani, Ryan J. “The Lasso Problem and Uniqueness.” Electronic Journal of Statistics, 7: 1456-1490, 2013. DOI: https://doi.org/10.1214/13-EJS815
Langlois, Gabriel P., and Darbon, Jerome. “A fast algorithm for solving the lasso problem exactly without homotopy using differential inclusions.” arXiv:2507.05562, 2025.
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
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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
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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
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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, Le, Xin Xing, and Youhui Ye. “A L-infinity Norm Counterfactual and Synthetic Control Approach.” Working Paper, Virginia Tech, 2025. arXiv: https://arxiv.org/abs/2510.26053. Reference implementation (Python): BioAlgs/LinfinitySC
Liao, Chengwang, Zhentao Shi, and Yapeng Zheng.
“A Relaxation Approach to Synthetic Control.”
Working Paper, The Chinese University of Hong Kong, 2026.
arXiv: https://arxiv.org/abs/2508.01793.
Reference implementation (Python package scmrelax):
metricshilab/scmrelax (installable from
PanJi-0/scmrelax); the Brexit / UK real-GDP
empirical application: YapengZheng/Relaxed_SC
Pouliot, Guillaume Allaire, Zhen Xie, and Ziyi Liu. “Degrees of Freedom and Information Criteria for the Synthetic Control Method.” Working Paper, 2022 (revised 2026). arXiv: https://arxiv.org/abs/2207.02943.
Xu, Yihong and Zhou, Quan. “Bayesian Synthetic Control with a Soft Simplex Constraint.” arXiv Working Paper, 2503.06454, 2025. URL: https://arxiv.org/abs/2503.06454
Li, Kathleen T. “Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods.” Journal of the American Statistical Association, 115(532): 2068-2083, 2020.
Doudchenko, Nikolay and Imbens, Guido W. “Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis.” arXiv Working Paper, 1610.07748, 2017. URL: https://arxiv.org/abs/1610.07748
Tian, Wei, Lee, Seojeong, and Panchenko, Valentyn. “Synthetic Controls with Multiple Outcomes.” The Econometrics Journal, utag005, 2026. DOI: https://doi.org/10.1093/ectj/utag005
Sun, Liyang, Ben-Michael, Eli, and Feller, Avi. “Using Multiple Outcomes to Improve the Synthetic Control Method.” The Review of Economics and Statistics, 2025. DOI: https://doi.org/10.1162/rest_a_01592
Sun, Liyang, Ben-Michael, Eli, and Feller, Avi. “Temporal Aggregation for the Synthetic Control Method.” AEA Papers and Proceedings, 114: 614-617, 2024. DOI: https://doi.org/10.1257/pandp.20241050
Bell, Suzanne O., Stuart, Elizabeth A., and Gemmill, Alison. “Texas’ 2021 Ban on Abortion in Early Pregnancy and Changes in Live Births.” JAMA, 330 (3): 281-282, 2023. DOI: https://doi.org/10.1001/jama.2023.10342
Agarwal, Anish, Shah, Devavrat, and Shen, Dennis. “Synthetic Interventions: Extending Synthetic Controls to Multiple Treatments.” Operations Research, 74(2): 840-859, 2025. DOI: https://doi.org/10.1287/opre.2025.1590
Kellogg, Maxwell, Mogstad, Magne, Pouliot, Guillaume A., and Torgovitsky, Alexander. “Combining Matching and Synthetic Control to Trade Off Biases From Extrapolation and Interpolation.” Journal of the American Statistical Association, 116(536): 1804-1816, 2021. DOI: https://doi.org/10.1080/01621459.2021.1979562
Zhu, Rong J. B. “Synthetic Regressing Control.” arXiv:2306.02584, 2023. https://arxiv.org/abs/2306.02584
Kim, Sungjin, Lee, Clarence, and Gupta, Sachin. “Bayesian Synthetic Control Methods.” Journal of Marketing Research 57(5):831-852, 2020.
Pinkney, Sean. “An Improved and Extended Bayesian Synthetic Control.” arXiv:2103.16244, 2021. DOI: https://doi.org/10.1177/0022243720936230 Reference code: https://github.com/clarencejlee/bscm
Bomze, Immanuel M., Peng, Bo, Qiu, Yuzhou, and Yıldırım, E. Alper. “On Tractable Convex Relaxations of Standard Quadratic Optimization Problems under Sparsity Constraints.” arXiv:2310.04340, 2023. https://arxiv.org/abs/2310.04340
Han, Shaoning, Gómez, Andrés, and Atamtürk, Alper. “The Equivalence of Optimal Perspective Formulation and Shor’s SDP for Quadratic Programs with Indicator Variables.” Operations Research Letters, 50(2): 195-198, 2022. https://www.sciencedirect.com/science/article/pii/S0167637722000141