Statistics, Biostatistics

1. Causal inference

2. Causal networks

3. Yule-Simpson paradox, Surrogate Paradox

4. Incomplete data analysis

2012-2015    Causal Inference    National Nature Science Foundation of China

2014-2017    Biostatistics      National Nature Science Foundation of China

1. Geng, Z., Liu, Y., Liu, C. C. and Miao, W. (2019) Evaluation of causal effects and local structure learning of causal networks. Ann. Rev. Statist. & Appl. 6, 103-124.

2. Geng,Z. (1992) Collapsibility of relative risks in contingency tables with a response variable. J. Royal Statist. Soc. B54, 585-93

3. Geng, Z. and Asano, Ch. (1993) Strong collapsibility of association measures in linear models. J. Royal Statist. Soc. B55, 741-747

4. Guo, J. H. and Geng, Z. (1995) Collapsibility of logistic regression coefficients. J. Royal Statist. Soc. B57, 263-267

5. Geng, Z.，Guo, J. H. and Fung W. K. (2002) Criteria for confounders in epidemiological studies. J. Royal Statist. Soc. B64, 3-15

6. Ma, Z., Xie, X. and Geng Z. (2006) Collapsibility of distribution dependence. J. Royal Statist. Soc. B 68, 127-133.

7. Xie, X., Geng, Z. and Zhao, Q. (2006) Decomposition of structural learning about directed acrylic graphs, Artificial Intelligence 170, 422-439.

8. Chen, H., Geng, Z. and Jia, J. (2007) Criteria for surrogate end points. J. Royal Statist. Soc. B 59, 911-932.

9. Xie, X. and Geng, Z. (2008) A recursive method for structural learning of directed acyclic graphs. J. Machine Learning Research, 9,459-483.

10. Jiang, Z. C., Ding, P. and  Geng, Z. (2016) Principal causal effect identification and surrogate endpoint evaluation by multiple trials. J Royal Statist. Soc. B78, 829-848.