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[短期课程]社会科学中的因果推断——密歇根州立大学Kenneth Frank教授

  课程题目:Whatwould it take to Change your Inference? Quantifying the Discourse about Causal Inferences in the Social Sciences

主讲人:Kenneth Frank教授

主讲人简介:Kenneth Frank现任美国密歇根州立大学咨询和教育心理学院、教育学院和农业与自然资源学院教授。1993年于芝加哥  大学教育学院获博士学位。他的研究运用严密的定量分析方法来探究作为社会组织的学校。主要关注领域有:社会网络分析、因果推断、多层模型。

  上课地点:京师大厦三层第一会议室
时间安排:2017年6月5日(周一)— 6月7日(周三)
上午08:30—11:30,下午14:00-17:00

课程简介
(1)Motivation 动机
Statistical inferences are often challenged because of uncontrolled bias.  There may be bias due to uncontrolled confounding variables or non-random selection into a sample.  We will answer the question about what it would take to change an inference by formalizing the sources of bias and quantifying the discourse about causal inferences in terms of those sources.  For example, we will transform challenges such as “But the inference of a treatment effect might not be valid because of pre-existing differences between the treatment groups” to questions such as “How much bias must there have been due to uncontrolled pre-existing differences to make the inference invalid?”

由于不受控制的偏差,统计推论往往受到挑战。由于不受控的混杂变量或非随机选择进入样本,可能会有偏差。 我们将回答关于通过形式化偏差来源和量化关于这些来源的因果推论的话语来改变推论的问题。例如,我们将转变例如“治疗效果的推论可能无效,因为治疗组之间有预先存在的差异”为“由不可控的预先存在的差异引发导致推论无效的偏差存在多少?”

(2)Approaches 方法
In part I we will use Rubin’s causal model to interpret how much bias there must be to invalidate an inference in terms of replacing observed cases with counterfactual cases or cases from an unsampled population.  In part II, we will quantify the robustness of causal inferences in terms of correlations associated with unobserved variables or in unsampled populations. Calculations for bivariate and multivariate analysis will be presented in thespreadsheet for calculating indices [KonFound-it!] with some links to  SPSS, SAS, and Stata.

第一部分,我们将使用鲁宾的因果模型来解释需要存在多少偏差可以在反事实案例或非抽样人群的案例取代观测案例方面使得推论无效。第二部分,我们将量化因果推论的稳健性与不可观测的变量和非抽样人群的相关性联系。双变量和多变量的计算分析将在电子表格 计算双变量和多变量分析的计算将在电子表格中提供,用于计算指标[KonFound-it!],其中包括SPSS,SAS和Stata的一些链接。

(3)Format 授课形式
The format will be a mixture of presentation, individual exploration, and group work. Participants may include graduate students and professors, although all must be comfortable with basic regression and multiple regression. Participants should bring their own laptop, or be willing to work with another student who has a laptop. Participants may choose to bring to the course an example of an inference from a published study or their own work, as well as data analyses they are currently conducting. 

形式将是讲座,个人探索和小组工作的结合。参与者可能包括研究生和教授,虽然所有人都必须熟悉基本回归和多元回归。参与者需要自备笔记本电脑,或者愿意和另一个有笔记本电脑的学生一起工作。参与者可以选择在会上分享将已经发表的研究成果或他们自己的成果,或者他们目前进行的数据分析情况。

  报名方式:欲报名学员请将姓名、专业、学校(单位),信息发送到xt_rcpy@bnu.edu.cn,注明:报名参加“因果推断”短期课程。
 


(中国基础教育质量监测协同创新中心)    



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