Causal AI – An Introduction

Learn the foundational components of Causal Artificial Intelligence
What you'll learn:
What Causality is
The relationship between Causation and Association
Why RCT's are the golden standard for Causal Inference
Main components of Pearlian Framework for Causality: Ladder of Causation, Causal Graphs, Do-calculus, Structural Causal Models
Machine Learning & Propensity Score-based Causal Effect Estimators
Causal Discovery (Algorithms)
How to estimate Average Causal Effects using observational data (covering the entire end-to-end process)
Requirements:
Basic Probability and Statistics knowledge
Description:
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!
Who this course is for:
Everyone interested in learning about Causal AI and who has some basic knowledge of Probability and Statistics
Particularly relevant for those working in the Data & Statistics field, like Data Scientists, Data Analysts, Decision Scientists, Statisticians, Data Engineers, Machine Learning Engineers, Computer Scientists, Business Intelligence Analysts, Quantitative Analysts, etc.
Those who want to be at the forefront of advancements in Data and AI for decision-making
Homepage

https://www.udemy.com/course/causal-ai-an-introduction/

Causal AI – An Introduction Fast Download
Causal AI – An Introduction Full Download