菲律宾环球360注册账号学术报告[2021] 076号

(高水平大学建设系列报告576号)

报告题目: On Mendelian Randomisation Mixed-Scale Treatment Effect Robust Identification  and Estimation for Causal Inference

报告人：刘中华 教授（香港大学）

报告时间：8月30日10:00-10:40

报告地点：腾讯会议933218641 　　　　　　　　　　　　　　　　　　　

报告内容：

Standard Mendelian randomization analysis can produce biased results if the genetic variant defining instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging an invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian Randomization Mixed-Scale Treatment Effect Robust Identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the invalid IV on the additive scale; and (ii) that the selection bias due to confounding does not vary with the invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroscedastic and thus varies with the invalid IV. Although assumptions (i) and (ii) have, respectively appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV subject to pleiotropy. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. For estimation, we propose a simple and consistent three-stage estimator that can be used as preliminary estimator to a carefully constructed one-step-update estimator, which is guaranteed to be more efficient under the assumed model. In order to incorporate multiple, possibly correlated and weak IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed MR MiSTERI method.

报告人简历：

Dr. Liu Zhonghua received his doctorate in biostatistics from Harvard University, advised by Prof. Xihong Lin. He worked on the Wall street (Morgan Stanley) as a quantitative strategist in NYC before joining HKU. His current research interests are: Statistical inference for massive data, Big Data Analytics, Causal Inference, Machine Learning, Signal Detection, Statistical Genetics and Genomics.

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                                            2021年8月28日