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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/causal-inference-2
课程评论：没有评论
课程名称：因果推断 2 课程概述： 本课程提供了关于因果推断高级主题的严格数学调查，适用于硕士水平。因果关系的推断在科学、医学、政策和商业等领域具有重要意义。本课程引介了过去35到40年间出现的统计文献，这些文献革命性地改变了统计学家和许多学科的应用研究者使用数据进行因果关系推断的方式。 我们将学习因果推断的高级主题，包括中介效应、主要分层、纵向因果推断、回归不连续性、干扰和固定效应模型。 课程大纲： - 模块7：中介效应简介 - 模块8：关于中介效应的更多内容 - 模块9：工具变量、主要分层与回归不连续性 - 模块10：纵向因果推断 - 模块11：干扰与固定效应 该课程将为学员提供深入的因果推断理论与应用知识，帮助他们在各自领域内有效地进行因果关系分析。
Name:Module 7: Introduction to Mediation
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Name:Module 8: More on Mediation
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Name:Module 9: Instrumental Variables, Principal Stratification, and Regression Discontinuity
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Name:Module 10: Longitudinal Causal Inference
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Name:Module 11: Interference and Fixed Effects
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This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study advanced topics in causal inference, including mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models.
