【报告摘要】In this talk, we introduce the concepts of posterior causal effects given observed evidence to quantify the causes of effects. The posterior causal effects provide a unified framework for deducing both effects of causes in prospective causal inference and causes of effects in retrospective causal inference. We describe the assumptions of no confounding and monotonicity, under which we prove identifiability of the multivariate posterior causal effects and provide their identification equations. Further, we discuss its applications in reliability importance measures and medical diagnosis. A numerical example is used to illustrate the proposed approach.

【报告人简介】LU Zitong is a final year PhD student at the Department of Systems Engineering, City University of Hong Kong. His main research interests include causal inference, root cause analysis, reliability, and sensitivity analysis. He has published in top journals like Biometrika, IEEE Transactions on Reliability, and Reliability Engineering & System Safety.