开始时间: 12/21/2023 持续时间: 5 weeks of study, 3-5 hours per week
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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/crash-course-in-causality
课程评论：没有评论
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!
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.
我们都听过“相关不等于因果”这句话。那么，什么是相等的因果关系？本课程旨在回答这个问题等等！在 5 周的时间里，您将了解如何定义因果效应、需要对您的数据和模型进行哪些假设，以及如何实施和解释一些流行的统计方法。学习者将有机会将这些方法应用于 R（免费统计软件环境）中的示例数据。在课程结束时，学习者应该能够： 1. 使用潜在结果定义因果效应 2. 描述关联和因果关系之间的区别 3. 用因果图表达假设 4. 实施几种类型的因果推理方法（例如匹配、工具变量，治疗加权的逆概率） 5. 确定每种统计方法都需要哪些因果假设所以加入我们......并亲自发现为什么用于估计因果效应的现代统计方法在如此多的研究领域中必不可少！
