Abstract: Artificial Intelligence has taken the world by storm – the data science world has focused predominantly on these predictive applications and machine learning techniques. But as organisations have embraced data driven decision making, the questions we need to answer are more intervention-based and this demands a different toolkit – causal inference. We are excited by the developments happening in academia and the rapid advancement of open source functionality and in this tutorial we will share our learning experience of applying these techniques in business.
By completing this workshop, you will develop an understanding of the core principles of causal inference and how it differs from AI. You will see how in this context often data is not enough, and incorporating business knowledge and understanding is key. You will also become familiar with tools to implement causal inference in your own decision making solutions.
Lesson 1: Why do we need causal inference?
Distinguishing between causal and prediction problems. At the end of this lesson you will be comfortable with the distinction between inference and prediction.
Lesson 2: What is causal inference?
We talk through a pragmatic workflow – the Causal Inference Recipe. A framework of steps to take for any causal inference question with observational data. You will be able to explain the theory of the core components of the recipe.
Lesson 3: How do you do causal inference?
We will talk through a selection of the open source tools for causal inference. We will then go through a notebook that uses the recipe step by step.
Bio: Jamie Hilton is a Senior Data Scientist at Jaguar Land Rover (JLR) with over 5 years of experience realising business value through data insights. At JLR his work focuses on driving digital transformation with data, helping the business to make the right decisions at the right time. Previously, he led advanced analytics initiatives as Head of Customer Science at Manchester-based e-commerce business THG. He holds a MA in Mathematics from the University of Cambridge.
Jamie is particularly passionate about the application of data science to the automotive and motorsport industries. In 2021, he worked with leading Formula 2 team Virtuosi Racing to deliver a competitive advantage by leveraging their data, having studied Advanced Motorsport Engineering at Cranfield University.