1. Austin, P.C., Jembere, N., Chiu, M. (2018). Propensity score matching and complex surveys. Statistical Methods in Medical Research, 27(4), pp. 1240–1257. DOI: 10.1177/0962280216658920
2. Baiocchi, M., Cheng, J., Small, D.S. (2014). Instrumental variable methods for causal inference. Statistics in Medicine, 33(13), pp. 2297–2340. DOI: 10.1002/sim.6254
3. Brumback, B.A., He, Z., Prasad, M., Freeman, M.C., Rheingans, R. (2014). Using structural‐nested models to estimate the effect of cluster‐level adherence on individual‐level outcomes with a three‐armed cluster‐randomized trial. Statistics in Medicine, 33(9), pp. 1490–1502. DOI: 10.1002/sim.6049
4. Caetano, C. (2015). A test of exogeneity without instrumental variables in models with bunching. Econometrica, 83(4), pp. 1581–1600. DOI: 10.3982/ecta11231
5. Cerna-Turoff, I., Maurer, K., Baiocchi, M. (2022). Pre-processing data to reduce biases: Full matching incorporating an instrumental variable in population-based studies. International Journal of Epidemiology, 51(6), pp. 1920–1930. DOI: 10.1093/ije/dyac097
6. Cheng, D., Li, J., Liu, L., Liu, J., Le, T.D. (2024). Data-driven causal effect estimation based on graphical causal modelling: A survey. ACM Computing Surveys, 56(5), pp. 1–37. DOI: 10.1145/3636423
7. DiPrete, T.A., Gangl, M. (2004). Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociological Methodology, 34(1), pp. 271–310. DOI: 10.1111/j.0081-1750.2004.00154.x
8. DuGoff, E.H., Schuler, M., Stuart, E. A. (2014). Generalizing observational study results: Applying propensity score methods to complex surveys. Health Services Research, 49(1), pp. 284–303. DOI: 10.1111/1475-6773.12090
9. Ehsanul Karim, M., Lei, Y. (2025). Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? PLOS One, 20(5), e0324639. DOI: 10.1371/journal.pone.0324639
10. Farbmacher, H., Huber, M., Lafférs, L., Langen, H., Spindler, M. (2022). Causal mediation analysis with double machine learning. The Econometrics Journal, 25(2), pp. 277–300. DOI: 10.1093/ectj/utac003
11. Felderer, B., Kueck, J., Spindler, M. (2023). Using double machine learning to understand nonresponse in the recruitment of a mixed-mode online panel. Social Science Computer Review, 41(2), pp. 461–481. DOI: 10.1177/08944393221095194
12. Felton, C., Stewart, B.M. (2024). Handle with care: A sociologist’s guide to causal inference with instrumental variables. Sociological Methods & Research, 55(1), pp. 3-50. DOI: 10.1177/00491241241235900
13. Griffin, B.A., Schuler, M.S., Cefalu, M., Ayer, L., Godley, M., Greifer, N., Coffman, D.L., McCaffrey, D.F. (2023). A tutorial for propensity score weighting for moderation analysis with categorical variables: An application examining smoking disparities among sexual minority adults. Medical Care, 61(12), pp. 836–845. DOI: 10.1097/mlr.0000000000001922
14. Hartman, E., Huang, M. (2024). Sensitivity analysis for survey weights. Political Analysis, 32(1), pp. 1–16. DOI: 10.1017/pan.2023.12
15. Hernández‐Díaz, S., Bateman, B.T., Palmsten, K., Schneeweiss, S., Huybrechts, K.F. (2020). Using nationally representative survey data for external adjustment of unmeasured confounders: An example using the NHANES data. Pharmacoepidemiology and Drug Safety, 29(9), pp. 1151–1158. DOI: 10.1002/pds.4946
16. Höfler, M., Lieb, R., Wittchen, H. (2007). Estimating causal effects from observational data with a model for multiple bias. International Journal of Methods in Psychiatric Research, 16(2), pp. 77–87. DOI: 10.1002/mpr.205
17. Huang, M., Pimentel, S. D. (2025). Variance-based sensitivity analysis for weighting estimators results in more informative bounds. Biometrika, 112(1), asae040. DOI: 10.1093/biomet/asae040
18. Huang, M.Y. (2024). Sensitivity analysis for the generalization of experimental results. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(4), pp. 900–918. DOI: 10.1093/jrsssa/qnae012
19. Inoue, K., Sakamaki, K., Komukai, S., Ito, Y., Goto, A., Shinozaki, T. (2025). Methodological tutorial series for epidemiological studies: Confounder selection and sensitivity analyses to unmeasured confounding from epidemiological and statistical perspectives. Journal of Epidemiology, 35(1), pp. 3–10. DOI: 10.2188/jea.je20240082
20. Kalton, G., Piesse, A. (2007). Survey research methods in evaluation and case–control studies. Statistics in Medicine, 26(8), pp. 1675–1687. DOI: 10.1002/sim.2796
21. Lee, K., Small, D.S., Rosenbaum, P.R. (2018). A powerful approach to the study of moderate effect modification in observational studies. Biometrics, 74(4), pp. 1161–1170. DOI: 10.1111/biom.12884
22. Leite, W.L., Stapleton, L.M., Bettini, E.F. (2019). Propensity score analysis of complex survey data with structural equation modeling: A tutorial with mplus. Structural Equation Modeling: A Multidisciplinary Journal, 26(3), pp. 448–469. DOI: 10.1080/10705511.2018.1522591
23. Li, L., Shen, C., Wu, A.C., Li, X. (2011). Propensity score-based sensitivity analysis method for uncontrolled confounding. American Journal of Epidemiology, 174(3), pp. 345–353. DOI: 10.1093/aje/kwr096
24. Lu, B., Ashmead, R. (2018). Propensity score matching analysis for causal effects with MNAR covariates. Statistica Sinica, 28, pp. 2005-2025. DOI: 10.5705/ss.202016.0320
25. Qin, X., Hong, G., Deutsch, J., Bein, E. (2019). Multisite causal mediation analysis in the presence of complex sample and survey designs and non-random non-response. Journal of the Royal Statistical Society Series A: Statistics in Society, 182(4), pp. 1343–1370. DOI: 10.1111/rssa.12446
26. Ren, J., Ma, T., Liu, S. (2025). Handling unobserved confounding for continuous sequential data via extracting key sub-intervals. Information Sciences, 717, 122325. DOI: 10.1016/j.ins.2025.122325
27. Rosenbaum, P.R. (2012). Testing one hypothesis twice in observational studies. Biometrika, 99(4), pp. 763–774. DOI: 10.1093/biomet/ass032
28. Rudolph, K.E., Colson, K.E., Stuart, E.A., Ahern, J. (2016). Optimally combining propensity score subclasses. Statistics in Medicine, 35(27), pp. 4937–4947. DOI: 10.1002/sim.7046
29. Rudolph, K.E., Stuart, E.A. (2018). Using sensitivity analyses for unobserved confounding to address covariate measurement error in propensity score methods. American Journal of Epidemiology, 187(3), pp. 604–613. DOI: 10.1093/aje/kwx248
30. Stockham, N., Washington, P., Chrisman, B., Paskov, K., Jung, J.-Y., Wall, D.P. (2022). Causal modeling to mitigate selection bias and unmeasured confounding in internet-based epidemiology of COVID-19: Model development and validation. JMIR Public Health and Surveillance, 8(7), e31306. DOI: 10.2196/31306
31. Tan, Z. (2010). Marginal and nested structural models using instrumental variables. Journal of the American Statistical Association, 105(489), pp. 157–169. DOI: 10.1198/jasa.2009.tm08299
32. Tchetgen Tchetgen, E.J., Wirth, K.E. (2017). A general instrumental variable framework for regression analysis with outcome missing not at random. Biometrics, 73(4), pp. 1123–1131. DOI: 10.1111/biom.12670
33. Trinquart, L., Erlinger, A.L., Petersen, J.M., Fox, M., Galea, S. (2019). Applying the E value to assess the robustness of epidemiologic fields of inquiry to unmeasured confounding. American Journal of Epidemiology, 188(6), pp. 1174–1180. DOI: 10.1093/aje/kwz063
34. Wang, L., Robins, J.M., Richardson, T.S. (2017). On falsification of the binary instrumental variable model. Biometrika, asw064. DOI: 10.1093/biomet/asx011
35. Wyss, R., Yanover, C., El‐Hay, T., Bennett, D., Platt, R.W., Zullo, A.R., Sari, G., Wen, X., Ye, Y., Yuan, H., Gokhale, M., Patorno, E., Lin, K.J. (2022). Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature. Pharmacoepidemiology and Drug Safety, 31(9), pp. 932–943. DOI: 10.1002/pds.5500
36. Zhou, X., Xie, Y. (2016). Propensity score–based methods versus MTE-based methods in causal inference: Identification, estimation, and application. Sociological Methods & Research, 45(1), pp. 3–40. DOI: 10.1177/0049124114555199

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