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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/causal-inference
课程评论：没有评论
课程名称：因果推断 课程概述：本课程提供了一门严谨的数学性的因果推断研究，适用于硕士阶段。因果推断在科学、医学、政策和商业等领域具有重要意义。课程介绍了过去35-40年内出现的因果推断统计文献，这些文献彻底改变了统计学家和应用研究人员在众多学科中使用数据推断因果关系的方式。 学生将学习如何收集数据以估计因果关系，区分因果关系与非因果关系，这一点并非总是显而易见。接下来，我们将研究和评估学生可以使用的各种方法，如匹配、倾向得分的子分类、治疗逆概率加权以及机器学习，来估计多种效果，例如平均治疗效果和对接受治疗者的影响。课程最后，我们讨论评估已做的一些假设的方法，并展望后续课程中将要探讨的扩展内容。 课程大纲： 模块1：关键概念 模块2：随机化推断 模块3：回归 模块4：倾向得分 模块5：匹配 模块6：专题讨论
Name:MODULE 1: Key Ideas
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Name:Module 2: Randomization Inference
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Name:MODULE 3: Regression
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Name:Module 4: Propensity Score
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Name:Module 5: Matching
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Name:Module 6: Special Topics
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This course offers a rigorous mathematical survey of 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 methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course.
