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所在平台: Udemy |
课程主页: https://www.udemy.com/course/causal-ai-an-introduction/
课程评论：没有评论
**课程名称：** 因果人工智能：全面入门 **课程概述：** 本课程旨在系统介绍因果人工智能（Causal AI）的核心概念和基础知识。在当今商业决策中，仅仅依靠相关性分析已不足以应对错综复杂的业务问题。理解不同决策对结果的影响，并从中选择最优方案，是商业决策的关键。然而，仅凭相关性模型无法深入探究因果关系。 该课程将重点介绍由 Judea Pearl 提出的开创性因果框架。这一框架提供了清晰、通用的方法，使我们能够使用观测数据来理解因果关系并估算因果效应。通过将 Judea Pearl 的工作与人工智能领域的最新进展相结合，因果人工智能应运而生。 因果人工智能的核心在于利用人工智能模型，尤其是基于观测数据来估算因果效应。传统上，企业主要依赖随机对照试验（RCT）和 A/B 测试来确定因果关系。然而，因果人工智能通过提供使用观测数据的工具，极大地扩展了这一能力，这对于在商业环境中更普遍的观测数据尤为重要。当实验难以实施或不切实际时，因果人工智能便成为利用现有数据进行决策的强大工具。 本课程将弥合数据和统计领域从业人员在因果技术方面的知识鸿沟。您将学习因果人工智能的基础组成部分，特别关注 Pearlian 框架，包括因果的阶梯（Ladder of Causation）、因果图（Causal Graphs）、Do-calculus 和结构因果模型（Structural Causal Models）。此外，课程还将深入探讨各种因果效应的估算技术，涵盖机器学习和倾向得分（propensity score）方法。最后，您还将学习如何获取因果图，即因果发现（Causal Discovery）的过程。 完成本课程后，您将掌握使用观测数据估算平均因果效应所需的所有工具。我们相信，所有从事数据和统计工作的人都应理解因果关系并掌握因果技术。及早掌握这一领域的知识将使您在同行中脱颖而出。本课程非常适合具备概率和统计学基础知识，并对因果人工智能感兴趣的学习者。
In this course, you'll learn the foundational components of Causal Artificial Intelligence (Causal AI). More and more people are starting to realise that correlation-focused models are not enough to answer our most important business questions. Business decision-making is all about understanding the effect different decisions have on outcomes, and choosing the best option. We can't understand the effect decisions have on outcomes with just correlations; we must understand cause and effect. Unfortunately, there is a huge gap of knowledge in causal techniques among people working in the data & statistics industry. This means that causal problems are often approached with correlation-focused models, which results in sub-optimal or even poor solutions. In recent years, the field of Causality has evolved significantly, particularly due to the work of Judea Pearl. Judea Pearl has created a framework that provides clear and general methods we can use to understand causality and estimate causal effects using observational data. Combining his work with advances in AI has given rise to the field of Causal Artificial Intelligence.Causal AI is all about using AI models to estimate causal effects (using observational data). Generally, businesses rely only on experimentation methods like Randomized Controlled Trials (RCTs) and A/B tests to determine causal effects. Causal AI now adds to this by offering tools to estimate causal effects using observational data, which is more commonly available in business settings. This is particularly valuable when experimentation is not feasible or practical, making it a powerful tool for businesses looking to use their existing data for decision-making.This course is designed to bridge the knowledge gap in causal techniques for individuals interested in data and statistics. You will learn the foundational components of Causal AI, with a specific focus on the Pearlian Framework. Key concepts covered include The Ladder of Causation, Causal Graphs, Do-calculus, and Structural Causal Models. Additionally, the course will go into various estimation techniques, incorporating both machine learning and propensity score-based estimators. Last, you'll learn about methods we can use to obtain Causal Graphs, a process called Causal Discovery.By the end of this course, you'll be fully equipped with all tools needed to estimate average causal effects using observational data. We believe that everyone working in the data and statistics field should understand causality and be equipped with causal techniques. By educating yourself early in this area, you will set yourself apart from others in the field. If you have a basic understanding of probability and statistics and are interested in learning about Causal AI, this course is perfect for you!
