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所在平台: Udemy |
课程主页: https://www.udemy.com/course/causal-inference-with-linear-regression-a-modern-approach/
课程评论：没有评论
课程名称：线性回归中的因果推断：现代方法 课程概述：线性回归是数据行业中最广泛使用的模型之一，但在因果推断方面常常被误解。许多情况下，其系数被错误地解释为因果效应。尽管传统假设（如外生性）经常被强调，但其真实含义却很少被理解。尽管很多人能背诵OLS的经典假设以证明系数“无偏”，但却难以明确这些论述的实际含义。面对因果推断的教育资源，很多内容都显得模糊、矛盾。 本课程分为两部分，旨在填补这种知识缺口，采用新颖的现代方法，准确教授何时以及如何将线性回归系数与因果效应联系起来。第一部分将讨论因果推断的基础概念，以帮助学员理解后续内容。在第二模块中，我们将深入探讨线性回归的机制，重点讲解如何通过普通最小二乘法计算线性回归系数。在第三模块中，我们介绍线性结构因果模型，您将了解到这些方程中的参数是我们在使用线性回归估计因果效应时真正感兴趣的内容，并讨论有哪些具体条件可以使线性回归系数成功恢复这些真实的因果参数。最后，在第四模块中，我们将探讨精心设计的鲁棒性检验和敏感性分析如何帮助您增强因果分析结果的可信度和支撑您的结论。 课程内容将澄清关于线性回归系数因果解释的一些常见误解。本课程的理论基础来自于高质量的因果推断研究，包括Angrist和Pischke、Carlos Cinelli、Chad Hazlett以及Judea Pearl等领先研究者的工作。而且，课程不仅有扎实的理论基础，还是为实际应用而设计的。为了巩固您的理解，我们将通过众多编码实例进行实操，每个模块还包括一个编码练习，以帮助您练习所讨论的技术。本课程适合具有基础概率论、线性代数和统计学知识的人士，熟悉编程（特别是Python）将为学习有所帮助。 如果您希望在因果推断中有效使用线性回归并脱颖而出，本课程非常适合您！
Linear regression is one of the most widely used models in the data industry-but also one of the most misunderstood when it comes to Causal Inference.Too often, its coefficients are wrongly interpreted as causal effects. Traditional assumptions like exogeneity are often emphasized, yet their true meaning is rarely understood. Many can recite the classic OLS assumptions under which coefficients are "unbiased," but struggle to articulate what they are actually unbiased for.And this isn't surprising. Educational sources on Linear Regression are extremely vague, ambiguous and often even contradictory when it comes to Causal Inference.In this 2-part course series, we fill that gap using a fresh and modern approach. You'll learn exactly how and when Linear Regression coefficients reflect causal effects.This first part starts by discussing the foundational concepts from Causal Inference that you'll need to understand to follow the remainder of the course.In the second module, we'll go deep into the mechanics of Linear Regression, with an emphasis on how Linear Regression coefficients are computed using Ordinary Least Squares.In module 3, we introduce Linear Structural Causal models, where you'll learn that the parameters in these equations are the ones we are actually interested in when estimating causal effects using Linear Regression. We then discuss the exact conditions under which Linear Regression coefficients succeed in recovering these true causal parametersFinally, in module 4, we explore how well-designed Robustness Tests and Sensitivity Analysis can help you build trust in your causal analysis results and better defend your conclusions.Along the way, we'll clarify some of the most common misconceptions about the causal interpretation of Linear Regression coefficients.Everything in this course is based on high-quality sources in Causal Inference, including the work of leading researchers like Angrist & Pischke, Carlos Cinelli, Chad Hazlett, and Judea Pearl.But beyond its strong theoretical foundation, this course is built for real-world application. To reinforce your understanding, we'll work through numerous coding examples, and each module includes a coding exercise to help you practice with the discussed techniques.This course is for anyone with a basic understanding of Probability Theory, Linear Algebra, and Statistics. Familiarity with programming is helpful, as coding examples and exercises will be in Python.So, if you want to stand out and truly understand how to use Linear Regression for Causal Inference correctly, this course is for you!
