讨论班 | ESL讨论班、学术交流研讨班、因果推断讨论班（2021/6/7-2021/6/13）
1、讨论班简介
ESL讨论班
针对新同学开展统计学习精要的学习，主讲机器学习知识。课程内容以《The Elements of Statistical Learning》为主，部分延伸内容需要参考辅助书目；
学术交流研讨班
针对博士生开展，主要形式为学术论文讨论交流。
因果推断讨论班
研究主题包括复杂数据的因果推断，精准治疗/决策，IV, DID, RDD, mediation等计量方法，异质性因果效应，因果机器学习，因果网络发现，非参检验，多源数据融合的因果分析和匹配、加权等经典方法的最新进展。
2、时间及地点
3、本期内容概述
ESL讨论班
内容预告
变分推断是一种逼近在贝叶斯推断和机器学习中出现的难解积分的方法，通常用于由观察变量未知参数和潜变量组成的概率图模型之中。本次讨论班将从经典的EM算法出发，讲述变分推断如何通过不断迭代优化证据下界来最大化观察变量的似然，最后将介绍变分推断的相关变体。
腾讯会议链接
会议主题：ESL讨论班
会议时间：2021/06/08-2021/06/29 19:00-21:30(GMT+08:00) 中国标准时间 - 北京, 每周 (周二)
点击链接入会，或添加至会议列表：
https://meeting.tencent.com/s/lK0zAsGa2lby
会议 ID：432 5678 9554
学术交流研讨班
内容预告
Heterozygosity Index for Low Coverage Whole Genome Sequencing Data
Abstract
Currently, the clinical application of lcWGS is mainly restricted to CNV-seq and some common genetic variations with clinic significance are undetected by lcWGS, including triploidy and ROHs. Herein, we introduced heterozygosity index (HI) as a rational measurement of heterozygosity for lcWGS, taking into account the distributions of MAF and sequence depth of SNPs. Based on the heterozygosity index (HI), HV-seq is recommended as a combination of approaches to detect heterozygosity variations (HV). Retrospective studies are also launched to verified the feasibility of HV-seq in detecting triploidy and ROHs.
腾讯会议链接
会议主题：学术交流研讨班
会议时间：2021/05/28-2021/08/27 16:00-18:00(GMT+08:00) 中国标准时间 - 北京, 每周 (周五)
点击链接入会，或添加至会议列表：
https://meeting.tencent.com/s/vwoS2FR3Lef0
会议 ID：591 2872 1234
因果推断讨论班
内容预告
Policy Learning With Observational Data
Abstract
application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.
Causal inference, social networks, and chain graphs
Abstract
Traditionally, statistical and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is practically infeasible to collect in most settings, and second, the models are high-dimensional and often too big to fit to the available data. In this paper we illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks using data from U.S. Supreme Court decisions between 1994 and 2004 and in simulations.
腾讯会议链接
会议主题：2021因果推断讨论班
会议时间：2021/06/11-2021/07/23 18:30-20:00(GMT+08:00) 中国标准时间 - 北京, 每周 (周五)
点击链接入会，或添加至会议列表：
https://meeting.tencent.com/s/srrKL94HijwT
会议 ID：674 5622 9494
