References
- Bhattacharya, R., Nabi, R., Shpitser, I., and Robins, J. M. (2019), “Identification in Missing Data Models Represented by Directed Acyclic Graphs,” in Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence.
- Bickel, P. J., Klaassen, C. A. J., Ritov, Y., and Wellner, J. A. (1993), Efficient and Adaptive Estimation for Semiparametric Models, Baltimore, MD: Johns Hopkins University Press.
- Chen, H. Y. (2007), “A Semiparametric Odds Ratio Model for Measuring Association,” Biometrics, 63, 413–421.
- Chen, H. Y. (2010), “Compatibility of Conditionally Specified Models,” Statistics & Probability Letters, 80, 670–677.
- Chen, J. Y., Ribaudo, H. J., Souda, S., Parekh, N., Ogwu, A., Lockman, S., Powis, K., Dryden-Peterson, S., Creek, T., Jimbo, W., Madidimalo, T., Makhema, J., Essex, M., and Shapiro, R. L. (2012), “Highly Active Antiretroviral Therapy and Adverse Birth Outcomes Among HIV-Infected Women in Botswana,” The Journal of Infectious Diseases, 206, 1695–1705. DOI: 10.1093/infdis/jis553.
- Højsgaard, S., Edwards, D., and Lauritzen, S. (2012), Graphical Models With R, Boston, MA: Springer.
- Lauritzen, S. L. (1996), Graphical Models, Oxford: Clarendon Press.
- Li, L., Shen, C., Li, X., and Robins, J. M. (2013), “On Weighting Approaches for Missing Data,” Statistical Methods in Medical Research, 22, 14–30. DOI: 10.1177/0962280211403597.
- Little, R. J. A. (1993), “Pattern-Mixture Models for Multivariate Incomplete Data,” Journal of the American Statistical Association, 88, 125–134.
- Little, R. J. A., and Rubin, D. B. (2014), Statistical Analysis With Missing Data (2nd ed.), New York: Wiley.
- Marra, G., Radice, R., Bärnighausen, T., Wood, S. N., and McGovern, M. E. (2017), “A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses,” Journal of the American Statistical Association, 112, 484–496. DOI: 10.1080/01621459.2016.1224713.
- Mohan, K., and Pearl, J. (2018), “Graphical Models for Processing Missing Data,” arXiv no. 1801.03583.
- Mohan, K., Pearl, J., and Tian, J. (2013), “Graphical Models for Inference With Missing Data,” in Advances in Neural Information Processing Systems, pp. 1277–1285.
- Mohan, K., Thoemmes, F., and Pearl, J. (2018), “Estimation With Incomplete Data: The Linear Case,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 5082–5088.
- Newey, W. K. (1990), “Semiparametric Efficiency Bounds,” Journal of Applied Econometrics, 5, 99–135. DOI: 10.1002/jae.3950050202.
- Robins, J. M. (1997), “Non-Response Models for the Analysis of Non-Monotone Non-Ignorable Missing Data,” Statistics in Medicine, 16, 21–37. DOI: 10.1002/(SICI)1097-0258(19970115)16:1<21::AID-SIM470>3.0.CO;2-F.
- Robins, J. M. and Gill, R. D. (1997), “Non-Response Models for the Analysis of Non-Monotone Ignorable Missing Data,” Statistics in Medicine, 16, 39–56. DOI: 10.1002/(SICI)1097-0258(19970115)16:1<39::AID-SIM535>3.0.CO;2-D.
- Robins, J. M., Rotnitzky, A., and Scharfstein, D. O. (2000), “Sensitivity Analysis for Selection Bias and Unmeasured Confounding in Missing Data and Causal Inference Models,” in Statistical Models in Epidemiology: The Environment and Clinical Trials, eds. M. E. Halloran and D. Berry, New York: Springer, pp. 1–94.
- Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994), “Estimation of Regression Coefficients When Some Regressors Are Not Always Observed,” Journal of the American Statistical Association, 89, 846–866. DOI: 10.1080/01621459.1994.10476818.
- Rotnitzky, A., and Robins, J. (1997), “Analysis of Semi-Parametric Regression Models With Non-Ignorable Non-Response,” Statistics in Medicine, 16, 81–102. DOI: 10.1002/(SICI)1097-0258(19970115)16:1<81::AID-SIM473>3.0.CO;2-0.
- Rotnitzky, A., Robins, J. M., and Scharfstein, D. O. (1998), “Semiparametric Regression for Repeated Outcomes With Nonignorable Nonresponse,” Journal of the American Statistical Association, 93, 1321–1339. DOI: 10.1080/01621459.1998.10473795.
- Sadinle, M., and Reiter, J. P. (2017), “Itemwise Conditionally Independent Nonresponse Modelling for Incomplete Multivariate Data,” Biometrika, 104, 207–220.
- Sadinle, M., and Reiter, J. P. (2018), “Sequential Identification of Nonignorable Missing Data Mechanisms,” Statistica Sinica, 28, 1741–1759.
- Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999), “Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models,” Journal of the American Statistical Association, 94, 1096–1120. DOI: 10.1080/01621459.1999.10473862.
- Shpitser, I. (2016), “Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random,” in Advances in Neural Information Processing Systems, pp. 3144–3152.
- Shpitser, I., Mohan, K., and Pearl, J. (2015), “Missing Data as a Causal and Probabilistic Problem,” in Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence.
- Sun, B. L., Liu, L., Miao, W., Wirth, K., Robins, J., and Tchetgen Tchetgen, E. J. (2018), “Semiparametric Estimation With Data Missing Not at Random Using an Instrumental Variable,” Statistica Sinica, 28, 1965–1983.
- Sun, B. L., and Tchetgen Tchetgen, E. J. (2018), “On Inverse Probability Weighting for Nonmonotone Missing at Random Data,” Journal of the American Statistical Association, 113, 369–379. DOI: 10.1080/01621459.2016.1256814.
- Tan, Z. (2019), “On Doubly Robust Estimation for Logistic Partially Linear Models,” arXiv no. 1901.09138.
- Tchetgen Tchetgen, E. J., Robins, J. M., and Rotnitzky, A. (2010), “On Doubly Robust Estimation in a Semiparametric Odds Ratio Model,” Biometrika, 97, 171–180. DOI: 10.1093/biomet/asp062.
- Tchetgen Tchetgen, E. J., Wang, L., and Sun, B. L. (2018), “Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference,” Statistica Sinica, 28, 2069–2088. DOI: 10.5705/ss.202016.0325.
- Tian, J. (2015), “Missing at Random in Graphical Models,” in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics.
- Tsiatis, A. (2006), Semiparametric Theory and Missing Data, New York: Springer.
- Tu, R., Zhang, C., Ackermann, P., Mohan, K., Hedvig Kjellström, and Zhang, K. (2019), “Causal Discovery in the Presence of Missing Data,” in The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1762–1770.
- van Buuren, S., and Groothuis-Oudshoorn, K. (2010), “mice: Multivariate Imputation by Chained Equations in R,” Journal of Statistical Software, 45, 1–68.
- Van der Vaart, A. W. (2000), Asymptotic Statistics, Cambridge: Cambridge University Press.
- Vansteelandt, S., Rotnitzky, A., and Robins, J. (2007), “Estimation of Regression Models for the Mean of Repeated Outcomes Under Nonignorable Nonmonotone Nonresponse,” Biometrika, 94, 841–860. DOI: 10.1093/biomet/asm070.
- Zhou, Y., Little, R. J. A., and Kalbfleisch, J. D. (2010), “Block-Conditional Missing at Random Models for Missing Data,” Statistical Science, 25, 517–532. DOI: 10.1214/10-STS344.