W. Miao, X. Shi, and E. Tchetgen Tchetgen. A confounding bridge approach for double negative control inference on causal effects. arXiv preprint arXiv:1808.04945, 2018b.
W. Miao, W. Hu, E. L. Ogburn, and X.-H. Zhou. Identifying effects of multiple treatments in the presence of unmeasured confounding. Journal of the American Statistical Association, pages 1-15, 2022.
S. A. Murphy. Optimal dynamic treatment regimes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65(2):331-355, 2003.
E. A. Nadaraya. On estimating regression. Theory of Probability & Its Applications, 9(1):141-142, 1964.
H. Pu and B. Zhang. Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83(2):318-345, 2021.
Z. Qi, R. Miao, and X. Zhang. Proximal learning for individualized treatment regimes under unmeasured confounding. Journal of the American Statistical Association, pages 1-14, 2023.
M. Qian and S. A. Murphy. Performance guarantees for individualized treatment rules. Annals of Statistics, 39(2):1180, 2011.
H. Qiu, M. Carone, E. Sadikova, M. Petukhova, R. C. Kessler, and A. Luedtke. Optimal individualized decision rules using instrumental variable methods. Journal of the American Statistical Association, 116(533):174-191, 2021.
A. Raghu, M. Komorowski, L. A. Celi, P. Szolovits, and M. Ghassemi. Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. In Machine Learning for Healthcare Conference, pages 147-163. PMLR, 2017.
J. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. *Mathematical modelling*, 7 (9-12):1393-1512, 1986.
J. M. Robins. Correcting for non-compliance in randomized trials using structural nested mean models. Communications in Statistics: Theory and Methods, 23(8):2379-2412, 1994.
J. M. Robins. Causal inference from complex longitudinal data. In Latent variable modeling and applications to causality, pages 69-117. Springer, 1997.
J. M. Robins, A. Rotnitzky, and L. P. Zhao. Estimation of regression coefficients when some regressors are not always observed. Journal of the American statistical Association, 89(427):846-866, 1994.
P. R. Rosenbaum and D. B. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41-55, 1983.
A. Rotnitzky, J. M. Robins, and D. O. Scharfstein. Semiparametric regression for repeated outcomes with nonignorable nonresponse. Journal of the American Statistical Association, 93(444):1321-1339, 1998.
D. O. Scharfstein, A. Rotnitzky, and J. M. Robins. Adjusting for nonignorable drop-out using semiparametric nonresponse models. Journal of the American Statistical Association, 94(448): 1096-1120, 1999.
D. W. Scott. Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons, 2015.
X. Shi, W. Miao, J. C. Nelson, and E. 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(2):521-540, 2020a.
X. Shi, W. Miao, and E. Tchetgen Tchetgen. A selective review of negative control methods in epidemiology. *Current Epidemiology Reports*, 7(4):190–202, 2020b.