[20] Jushan Bai and Serena Ng. Matrix completion, counterfactuals, and factor analysis of missing data, 2020.
[21] Jushan Bai and Serena Ng. Matrix completion, counterfactuals, and factor analysis of missing data. Journal of the American Statistical Association, 116(536):1746-1763, 2021.
[22] Eric Bair, Trevor Hastie, Debashis Paul, and Robert Tibshirani. Prediction by supervised principal components. Journal of the American Statistical Association, 101(473):119-137, 2006.
[23] Eli Ben-Michael, Avi Feller, and Jesse Rothstein. The augmented synthetic control method, 2020.
[24] Aldo Gael Carranza, Sanath Kumar Krishnamurthy, and Susan Athey. Flexible and efficient contextual bandits with heterogeneous treatment effect oracles. In International Conference on Artificial Intelligence and Statistics, pages 7190–7212. PMLR, 2023.
[25] Gary Chamberlain. Panel data. In Z. Griliches† and M. D. Intriligator, editors, Handbook of Econometrics, volume 2, chapter 22, pages 1247-1318. Elsevier, 1 edition, 1984. URL https://EconPapers.repec.org/RePEc:eee:ecochp:2-22.
[26] Jiafeng Chen. Synthetic control as online linear regression. *Econometrica*, 91(2):465-491, 2023.
[27] Victor Chernozhukov, Kaspar Wuthrich, and Yinchu Zhu. Practical and robust  $t$ -test based inference for synthetic control and related methods, 2020.
[28] Andrew Chesher. The effect of measurement error. Biometrika, 78(3):451-462, 1991.
[29] Sayak Ray Chowdhury and Aditya Gopalan. On kernelized multi-armed bandits. In International Conference on Machine Learning, pages 844–853. PMLR, 2017.
[30] Ben Chugg, Hongjian Wang, and Aaditya Ramdas. A unified recipe for deriving (time-uniform) pac-bayes bounds. arXiv preprint arXiv:2302.03421, 2023.
[31] Abhirup Datta and Hui Zou. Cocolasso for high-dimensional error-in-variables regression. 2017.
[32] Victor H de la Peña, Michael J Klass, and Tze Leung Lai. Self-normalized processes: exponential inequalities, moment bounds and iterated logarithm laws. 2004.
[33] Victor H de la Pena, Michael J Klass, and Tze Leung Lai. Pseudo-maximization and self-normalized processes. 2007.
[34] Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, and Matt Taddy. Accurate inference for adaptive linear models. In International Conference on Machine Learning, pages 1194-1203. PMLR, 2018.
[35] N. Doudchenko and G. Imbens. Balancing, regression, difference-in-differences and synthetic control methods: A synthesis. NBER Working Paper No. 22791, 2016.
[36] Walter Enders. Applied econometric time series. John Wiley & Sons, 2008.
[37] Jianqing Fan, Weichen Wang, and Yiqiao Zhong. An  $\ell_{\infty}$  eigenvector perturbation bound and its application to robust covariance estimation. Journal of Machine Learning Research, 18(207): 1-42, 2018.
[38] Vivek Farias, Ciamac Moallemi, Tianyi Peng, and Andrew Zheng. Synthetically controlled bandits. arXiv preprint arXiv:2202.07079, 2022.
[39] Iván Fernández-Val, Hugo Freeman, and Martin Weidner. Low-rank approximations of nonseparable panel models, 2020.
[40] Jorge I Figueroa-Zuniga, Cristian L Bayes, Víctor Leiva, and Shuangzhe Liu. Robust beta regression modeling with errors-in-variables: a bayesian approach and numerical applications. Statistical Papers, pages 1–24, 2022.