報告題目：Focused Generalized Method of Moments for Structural Learning in High-dimensional Causal Graphical Models
報 告 人：Li Changcheng 博士後，賓夕法尼亞州立大學
報告時間：2019年12月10日 13：30-14：30
報告地點：數學樓一樓第二報告廳
報告摘要：
In this paper, we propose a new constraint-based causal structural learning algorithm for high-dimensional Gaussian linear causal graphical models. Existing constraint-based approaches like the PC algorithm remove edges between vertices by carrying conditional independence tests on all possible candidates of d-separation sets. This can be computationally expensive and have exponential worst-case complexity. To tackle these issues, we propose a regularized approach called Focused Generalized Method of Moments (FGMM) to identify d-separation sets between vertices in this paper. Regularized approaches have been used to identify Markov blankets in causal graphical models. However, Markov blankets contain spouses besides true neighbors, which also need to be removed by searching d-separation sets. Distinguished from existing regularized approaches, the FGMM approach utilizes the moment conditions to identify d-separation sets directly. Furthermore, we propose an iterative linear approximation algorithm to solve the optimization problem in the FGMM approach efficiently. We further propose skeleton and structural learning algorithms based on the FGMM method, and establish the consistency of the FGMM algorithm in high-dimensional settings. We further conduct Monte Carlo simulations on various benchmark networks and show advantages of the proposed FGMM algorithm both in accuracy and speed.
報告人簡介：
Li Changcheng，賓夕法尼亞州立大學統計系博士後，于北京大學獲得學士、碩士學位，賓夕法尼亞州立大學獲得統計學博士學位，主要研究方向為高維統計。
