1Rubin D B. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educat Psychology, 1974, 66:688-701
2Holland P W. Statistics and causal inference. J Amer Statist Asso, 1986, 81:945-970
3Pearl J. Causal diagrams for empirical research (with discussion). Biometrika, 1995, 82:669-710
4Lauritzen SL, Richardson T S. Chain graph models and their causal interpretation (with discussion). J R Statist Soc B, 2002, 64:321-361
5Miettinen O S, Cook E F. Confounding: essence and detection. Amer J Epid, 1981, 114:593-603
6Greenland S, Robins J M, Pearl J. Confounding and collapsibility in causal inference. Statist Sci, 1999,14:29-46
7Geng Z, Guo J H, Fung W K. Criteria for confounders in epidemiological studies. J R Statist Soc B, 2002,64:3-16
8Geng Z, Li G. Conditions for confounding and collapsibility without knowledge of completely constructed causal diagrams. Scand J Statist, 2002, 29:169-181
9Stone R. The assumptions on which causal inferences rest. J R Statist Soc B, 1993, 55:455-466
10Rosenbaum P R, Rubin D B. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70:41-55
5VELLAISAMY P,VIJAY V. Collapsibility of regression coefficients and its extensions[J].Journal of Statistical Planning and Inference, 2008,138 : 982-994.
6XIANCHAO XIE, ZONGMING MA, ZHI GENG. Some association measures and their collapsibility[J]. Statistica Sinica, 2008, 18:1165-1183.
7JIANHUA GUO, ZHI GENG. Collapsibility of Logistic Regression Coefficients[J]. J R Statist Soe B, 1995,57:263-267.
8PEARL J. Causality: Models, Reasoning and Inference[M]. UK: Cambridge, 2000.
9NEAPOLITAN R E. Learning Bayesian Networks [ M].Upper Saddle River, N J: Prentice Hall, 2004.
10RICHARDSON T S, SPIRTES P. Ancestral graph Markov models[J]. Ann StatiSt, 2002, 30(4): 962-1030.
