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所在平台: Udemy |
课程主页: https://www.udemy.com/course/identifying-causal-effects-for-data-scientists/
课程评论：没有评论
课程名称：数据科学因果推断中的因果效应识别 课程概述：在数据科学的职业生涯中，最常被问到的问题就是：“X的影响是什么？”在许多角色中，这几乎是唯一会被问到的问题。因此，学习如何准确回答这个问题非常重要。本课程教授如何识别“处理效应”或“因果效应”，并教你如何从基本原则出发思考这一问题。你将学会提出以下问题：数据本身说明了什么？我对世界的了解与数据有什么不同？当我将这些知识与数据结合时，会发生什么？ 本课程不教授固定的方法或“食谱式”的程序，而是通过逐步的思考方式帮助你解决识别问题。具体而言，课程涵盖了影响处理效应的各种弱假设和强假设，其中常见的问题是：“处理效应是正的吗？”我们将学习无假设的数据能告诉我们多少关于处理效应的信息，以及常见假设（如随机处理分配、条件独立假设、排除限制和重复测量假设等）能给我们提供的学习内容。此外，课程还介绍了许多在其他课程中可能看不到的假设，如单调工具变量、单调混杂、单调处理选择、单调处理响应和单调趋势等。 该课程包含讲义，便于逐步审阅数学内容，同时配有测验和作业，让学员能够练习和应用所学的材料。 我的背景：我拥有威斯康星大学麦迪逊分校的经济学博士学位，主要在科技行业工作，目前是一名首席数据科学家，专注于需求建模和实验分析问题，这两者都是处理效应估算的实例。我来自阳光明媚的美国加利福尼亚州圣地亚哥。希望你尝试预览课程并注册完整课程！我随时欢迎提问。 - Zach
The most common question you'll be asked in your career as a data scientist is: What was/is/will be the effect of X? In many roles, it's the only question you'll be asked. So it makes sense to learn how to answer it well.This course teaches you how to identify these "treatment effects" or "causal effects". It teaches you how to think about identifying causal relationships from first principles. You'll learn to ask:What does the data say by itself?What do I know about the world that the data doesn't know?What happens when I combine that knowledge with the data?This course teaches you how to approach these three questions, starting with a blank page. It teaches you to combine your knowledge of how the world works with data to find novel solutions to thorny data analysis problems.This course doesn't teach a "cookbook" of methods or some fixed procedure. It teaches you to think through identification problems step-by-step from first principles. As for specifics:This course takes you through various weak assumptions that bound the treatment effect-oftentimes, the relevant question is just: "Is the treatment effect positive?"-and stronger assumptions that pin the treatment effect down to a single value. We learn what the data alone-without any assumptions-tells us about treatment effects, and what we can learn from common assumptions, like:Random treatment assignment (Experimentation)Conditional independence assumptions (Inverse propensity weighting or regression analysis)Exclusion restrictions (Instrumental variable assumptions)Repeated Measurement assumptionsParallel Trends (Difference-in-difference)And many assumptions you will probably not see in other courses, like:Monotone instrumental variablesMonotone confoundingMonotone treatment selectionMonotone treatment responseMonotone trends(Why do they all include "monotone" in the name? The answer to that question is beyond the scope of this course.)Lectures include lecture notes, which make it easy to review the math step by step. The course also includes quizzes and assignments to practice using and applying the material.My background: I have a PhD in Economics from the University of Wisconsin - Madison and have worked primarily in the tech industry. I'm currently a Principal Data Scientist, working mainly on demand modeling and experimentation analysis problems-both examples of treatment effect estimation! I am from sunny San Diego, California, USA. I hope you'll try the Preview courses and enroll in the full course! I'm always available for Q/A.-Zach
