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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/crash-course-in-causality
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课程名称：因果关系速成课程：从观察数据推断因果效应 课程概述： 我们都听过“相关性不等于因果关系”这句话。那么，什么才等于因果关系呢？本课程旨在回答这个问题以及更多相关内容！在为期5周的课程中，学员将学习因果效应的定义、对数据和模型的必要假设，以及如何实施和解释一些流行的统计方法。学员将有机会在R语言（一个免费的统计软件环境）中应用这些方法于示例数据。 完成本课程后，学员应能： 1. 使用潜在结果定义因果效应 2. 描述关联与因果之间的区别 3. 使用因果图表达假设 4. 实施几种因果推断方法（如匹配、工具变量、逆概率处理加权） 5. 确定每种统计方法所需的因果假设 加入我们，探索现代统计方法在各个研究领域中对估计因果效应的重要性！ 课程大纲： 1. 欢迎与因果效应介绍：本模块着重于使用潜在结果定义因果效应。强调设置/操控值与条件变量之间的区别，并介绍关键的因果识别假设。 2. 混淆与有向无环图（DAG）：本模块介绍有向无环图，通过理解这些图的各种规则，学员可以识别一组变量是否足以控制混淆。 3. 匹配与倾向评分：概述了估计因果效应的匹配方法，包括直接在混淆因子上匹配和基于倾向评分的匹配，并通过R语言中的数据分析示例进行说明。 4. 逆概率处理加权（IPTW）：介绍逆概率处理加权作为估计因果效应的方法，并通过R语言中的IPTW数据分析进行说明。 5. 工具变量方法：本模块专注于在随机试验中非依从性及观察研究中使用工具变量进行因果效应估计，并通过R语言中的工具变量分析进行说明。
Name:Welcome and Introduction to Causal Effects
Description:This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.
Name:Confounding and Directed Acyclic Graphs (DAGs)
Description:This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.
Name:Matching and Propensity Scores
Description:An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.
Name:Inverse Probability of Treatment Weighting (IPTW)
Description:Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.
Name:Instrumental Variables Methods
Description:This module focuses on causal effect estimation using instrumental variables in both randomized trials with non-compliance and in observational studies. The ideas are illustrated with an instrumental variables analysis in R.
We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!
