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北京大学社会研究中心与北京大学-密歇根大学学院                  


2018年暑期课程招生简章




北京大学社会研究中心与北京大学-密歇根大学学院将于2018年7月-8月在北京大学联合举办为期一个月的第12届暑期强化课程。任课教师均为密大和北美高校的著名教授。欢迎北大和国内高校、研究单位的教师、研究人员、硕、博研究生和高年级本科生申请本期暑期课程。课程要求学员掌握基本的统计概念和知识,熟悉回归分析模型。具体课程和任课教师介绍如下:


一、课程安排及介绍:

1、课程名称:

Causal Inference and Causal Mediation Analysis 《因果推论和因果机制分析》

时间:2018年7月9日至20日

课时:36学时     招生人数:80人 工作语言:英语

主讲教师:Guanglei Hong教授和Stephen Raudenbush教授

Guanglei Hong is Associate Professor with tenure in the Comparative Human Development Department and the Committee on Education at the University of Chicago. She is an active member of the quantitative methodology community at the University. Dr. Hong develops and applies causal inference theories and methods for evaluating educational and social policies and programs in multi-level, longitudinal settings. Her work is currently focused on developing concepts and methods for analyzing causal mediation mechanisms in multisite randomized trials. She has received research funding from the National Science Foundation, the Institute of Education Sciences of the U.S. Department of Education, the William T. Grant Foundation, the Spencer Foundation, and the Social Sciences and Humanities Research Council of Canada.

Her research monograph “Causality in a social world: Moderation, mediation, and spill-over” was published by John Wiley & Sons in July 2015. The book clarifies for applied researchers the theoretical concepts of moderated effects, mediated effects, and spill-over effects. It systematically introduces innovative statistical strategies for investigating these causal effects and aims to make them readily accessible to a broad audience. A major emphasis is placed on explicating and evaluating, in the context of real applications drawn from social sciences, education, and health research, the assumptions required for relating causal parameters of interest to empirical data given a specific research design. The book is accompanied by data examples and statistical programs for the new causal inference methods. Her other publications have appeared in statistics, education, and psychology journals including the Journal of the American Statistical Association, the Journal of Educational and Behavioral Statistics, Educational Evaluation and Policy Analysis, the Journal of Research on Educational Effectiveness, Psychological Methods, and Developmental Psychology, among others. She was Guest Editor for the Journal of Research on Educational Effectiveness special issue on the statistical approaches to studying mediator effects in education research published in 2012.

Stephen Raudenbush is the Lewis-Sebring Distinguished Service Professor in the Department of Sociology, the College and the Harris School of Public Policy Studies and Chairman of the Committee on Education at the University of Chicago. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. He received the American Educational Research Association award for Distinguished Contributions to Educational Research.

He is interested in statistical models for child and youth development within social settings such as classrooms, schools, and neighborhoods. He is best known for his work developing hierarchical linear models, with broad applications in the design and analysis of longitudinal and multilevel research. He is currently studying the development of literacy and math skills in early childhood with implications for instruction; and methods for assessing school and classroom quality.

网址:https://qrm.uchicago.edu/directory/guanglei-hong  and https://sociology.uchicago.edu/directory/stephen-raudenbush

课程介绍:

The goal of this two-week course is to equip students with basic knowledge and analytic skills in causal inference and causal mediation analysis. During the first week, the course will introduce the potential outcomes framework, clarify the assumptions required for identifying causal relationships in experimental and observational studies, and discuss the potential pitfalls of using ANCOVA or multiple regression to estimate a causal effect. The course will then focus on a number of propensity score based methods including matching, stratification, and weighting. Several econometric methods for causal inference will receive a brief review before the end of the first week. The second week will start with the topic about identifying the average treatment effect in a multisite randomized trial, revealing that treatment randomization at each site does not guarantee an unbiased analytic result even when there is perfect compliance. Further challenges and solutions will be discussed in the context of noncompliance. The course will then introduce causal mediation analysis in single-site and multisite studies, contrasting several alternative analytic strategies, and focusing on a new weighting-based approach to mediation analysis. Last but not least, the course will conclude with a discussion of sensitivity analysis for assessing the potential impact of hidden bias.

讲座介绍:

报告人:Stephen Raudenbush教授将做2次公开学术报告,时间待定。

(1) Does Schooling Increase or Reduce Social Inequality?

(2) Some New Tools for Causal Inference in Multilevel Research


2、课程名称:

General Designs and Sensitivity Analysis for Causal Inference 《因果推论方法的研究设计和敏感性分析》

时间:2018年7月23日至8月3日

课时:36学时      招生人数:80人 工作语言:英语

主讲教师:Kenneth Frank教授

Kenneth Frank received his Ph.D. in measurement, evaluation and statistical analysis from the School of Education at the University of Chicago in 1993.  He is currently a professor in Counseling, Educational Psychology and Special Education as well as in Fisheries and Wildlife and adjunct in Sociology at Michigan State University.  His substantive interests include the study of schools as social organizations and the social embeddedness of natural resource use.  His substantive areas are linked to several methodological interests: social network analysis, causal inference and multi-level models. His publications include quantitative methods for representing relations among actors in a social network, robustness indices for inferences, and the effects of social capital in schools and other social contexts.  He teaches general introductory courses in research methods and quantitative methods as well as advanced courses in multivariate analysis and seminars in social network analysis and causal inference.  Ken’s current projects include a study of the effects of the Michigan Merit Curriculum on educational outcomes and how knowledge about climate change diffuses to policy-makers and educators.

网址:https://msu.edu/user/k/e/kenfrank/web/index.htm

课程介绍:

There is currently great debate regarding the basis for causal inferences across the social sciences. Can we make causal inferences only from experiments? What about ethical or logistical limitations, or concerns that the experimental paradigm is artificial because of the necessity for extreme control over conditions? On the other hand, though observational studies are applied to natural conditions, can we rely on statistical control to make causal inferences? What about unmeasured, or unrecognized confounding factors? At what point does a statistical inference sustain a causal inference? Answers to these questions are more than merely academic and philosophical. For example they have immediate implications for policy-making regarding the implementation of innovations.

To address questions such as the above this course will explore causal inference from the perspectives of statistics and philosophy of science. We will begin with a comparison of causal inferences in the social sciences with those of the experimental sciences. Drawing on eclectic readings (Manski, Heckman, Rubin, Holland, Pearl, Shadish, Cook, Campbell Sobel, Dawid), we will use concepts such as the counterfactual, homogeneity of units and internal and external validity to describe causal inference. Furthermore, we will discuss statistical techniques such as propensity score matching and instrumental variables that might be used to improve the likelihood of valid inferences. Finally, we will use recent work to quantify how robust inferences are to potential threats the validity.

In the first half the course I will present methods including regression, propensity score matching, instrumental variables, regression discontinuity, random versus fixed effects, and sensitivity analysis. In the second half of the course we will turn to intensive projects or readings. Students will be required to present and provide an intensive critique of an article featuring complex issues of causal inference. For the final project students will need to develop work toward a publishable paper, either in the form of new method, or existing method applied to data of the students’ interest. Students may work in groups, with indications for who had primary responsibility for what work.


二、申请程序:

1.报名日期2018年4月1日-2018年5月20日。请登录网址http://www.oir.pku.edu.cn/umich 在线报名。

2. 2018年5月31日之前招生小组审核报名材料,并通知学生录取结果。

3. 学生收到录取通知(电子邮件)后,于2018年6月1日-10日缴费(具体办法和要求将在“缴费通知”中说明),并打印保管好缴费收据。

4. 在开课报到时,凭缴费收据领取正式通知书和北大开具的票据。请注意,非北大学生:中央非税收入统一票据;北大学生:中央行政事业单位资金往来结算票据(北大学生票据抬头只能写个人姓名)。


三、课程培训费/资料费:

非北大籍学生(含港澳台):1,200元/人/每门课程。 教师:1,500元/人/每门课程。

北大籍学生:1,000元/人/每门课程。

海外学员:2,000元/人/每门课程。


四、作业与考试:

课堂讨论、作业和考试由任课教师安排。


五、上课时间和地点:

上课时间:2018年7月9日-8月3日。

上课地点:北京大学(上课教室另行通知)


六、证书:

1、正式录取的学生学完规定的课时,完成必须的作业,通过考试后可获得规定的学分和由北京大学出具的成绩证明。

2、凡参加暑期课程的学员,完成必须的作业,通过考试后将获得由授课教师签名的单科结业证书


七、学习期间食宿:

学院不负责解决学生学习期间的食宿问题。食宿、生活等费用自理。


八、教材:教材、讲义等课程材料费用自理。


有关招生情况、师资、课程介绍、时间安排等,请登录网站:http://www.oir.pku.edu.cn/umich/查询。如果您有任何关于课程和申请过程的问题,欢迎与我们联系。

电话:010-6276-6210   传真:010-6276-7623 电邮:pku.um@pku.edu.cn

联系人:李老师

地址:北京大学廖凯原楼2号楼2-405室