耿直 - 北京师范大学-香港浸会大学

个人简介

耿直教授1989年于日本九州大学获理学博士学位，博士毕业后回国，到北京大学数学科学学院概率统计系任教30余载至2021年，是国内学界因果推断研究领域的先行者和实践家。现任北京工商大学数学与统计学院教授，兼任北京生物医学统计与数据管理研究会（BBA）理事长。耿教授1996年当选为国际统计学会（ISI）会士，曾任国务院学位委员会统计学科评议组成员，国家统计局统计咨询委员会委员，中国现场统计研究会理事长、中国数学学会概率统计学会理事长、中国统计学会副会长。耿教授曾被评为国家教委跨世纪优秀人才并荣获国务院政府特殊津贴，1998年获得国家杰出青年基金项目，曾获得国家统计局科技进步一等奖，国家教委科技进步奖二等奖、全国统计科学研究优秀成果奖一等奖等多项奖励。

研究领域

因果推断、不完全数据统计分析、生物医学统计等

近期论文

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Lu, Z., Geng, Z., Li, W., Zhu, S. and Jia, J. (2022) Evaluating causes of effects by posterior effects of causes. Biometrika. Fang, Z. Y., Liu, Y., Geng, Z., Zhu, S. Y. and He, Y. B. (2022) A local method for identifying causal relations under Markov equivalence. Artificial Intelligence 305. Xie, F., He, Y., Geng, Z., Chen, Z., Hou, R. and Zhang, K. (2022) Testability of instrumental variables in linear non-Gaussian acyclic causal models. Entropy 24, 512. Li, H., Jia, J., Yan, R., Xue, F. and Geng, Z. (2021) A causal data fusion method for the general exposure and outcome Statistics in Medicine 41, 328-339. Li, W., Geng, Z. and Zhou, X. H. (2021) Causal mediation analysis with sure outcomes of random events model. Statistics in Medicine 40, 3975-3989. Ma, L. Q., Yin, Y. J., Liu, L., Geng, Z. (2021) On the individual surrogate paradox. Biostatistics 22, 97-113. Liu, Y., Fang, Z. Y., He, Y. B., Geng, Z. and Liu, C. C. (2020) Local causal network learning for finding pairs of total and direct effects. J Mach Learn Res. 21 (148), 1-37. Yin, Y. J., Liu, L., Geng, Z. and Luo, P. (2020) Novel criteria to exclude the surrogate paradox and their optimalities. Scand J Statist. 47, 84-103. Li, H., Miao, W., Cai, Z., Liu, X., Zhang, T., Xue, F. and Geng, Z. (2020) Causal data fusion methods using summary-level statistics for a continuous outcome. Statistics in Medicine 39, 1054-1067. Li H, Geng Z, Sun X, Yu Y, Xue F. (2020) A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology[J]. BMC genetics, 2020, 21(1): 1-12. Liu, Y., Fang, Z. Y., He, Y. B. and Geng, Z. (2020) Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects. Uncertainty in Artificial Intelligence (UAI)ß290-299.2 Kuang, K., Li, L., Geng, Z., Xu, L., Zhang, K., Liao, B., Huang, H., Ding, P., Miao, W. and Jiang,Z. (2020) Causal inference. Engineering 6, 253-263. Li, W., Jiang, Z. C., Geng, Z. and Zhou, X. H. (2018) Identification of causal effects with laten-t confounding and classical additive errors in treatment. Biometrical Journal 60, 498-515, DOI:10.1002/bimj.201700048 Luo, P., Cai, Z. and Geng, Z. (2019) Criteria for multiple surrogates. Statistica Sinica 29, 1343-1366. Liu, Y., Cai, Z., Liu, C. C. and Geng, Z. (2019) Local learning approaches for finding effects of aspecified cause and their causal paths. ACM Trans Intellig Syst Tech. 10, 49:1-49:15. 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. Miao, W., Geng, Z. and Tchetgen Tchetgen, E. (2018) Identifying causal effects with proxy variables of an unmeasured confounder. Biometrika 105, 987-993.