A Crash Course in Causality: Inferring Causal Effects from Observational Data

开始时间: 04/04/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew


课程主页: https://www.coursera.org/learn/crash-course-in-causality

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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!

因果关系崩溃课程:从观察数据推断因果效应:我们都听过“相关不等于因果关系”这一短语。那么,等于因果关系又是什么呢?本课程旨在回答该问题以及更多问题! 在5周的时间里,您将学习如何定义因果关系,关于数据和模型的哪些假设是必需的,以及如何实现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关系 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 因此,请加入我们……,您会发现为什么在如此众多的研究领域中,估算因果效应的现代统计方法是必不可少的!





We have all heard the phrase “correlation does not equal causation.” What, then, does equal causati