Book Image

Causal Inference and Discovery in Python

By : Aleksander Molak
4.7 (9)
Book Image

Causal Inference and Discovery in Python

4.7 (9)
By: Aleksander Molak

Overview of this book

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.
Table of Contents (21 chapters)
Part 1: Causality – an Introduction
Part 2: Causal Inference
Part 3: Causal Discovery

Causality – Hey, We Have Machine Learning, So Why Even Bother?

Our journey starts here.

In this chapter, we’ll ask a couple of questions about causality.

What is it? Is causal inference different from statistical inference? If so – how?

Do we need causality at all if machine learning seems good enough?

If you have been following the fast-changing machine learning landscape over the last 5 to 10 years, you have likely noticed many examples of – as we like to call it in the machine learning community – the unreasonable effectiveness of modern machine learning algorithms in computer vision, natural language processing, and other areas.

Algorithms such as DALL-E 2 or GPT-3/4 made it not only to the consciousness of the research community but also the general public.

You might ask yourself – if all this stuff works so well, why would we bother and look into something else?

We’ll start this chapter with a brief discussion of the history of causality. Next, we’ll consider a couple of motivations for using a causal rather than purely statistical approach to modeling and we’ll introduce the concept of confounding.

Finally, we’ll see examples of how a causal approach can help us solve challenges in marketing and medicine. By the end of this chapter, you will have a good idea of why and when causal inference can be useful. You’ll be able to explain what confounding is and why it’s important.

In this chapter, we will cover the following:

  • A brief history of causality
  • Motivations to use a causal approach to modeling
  • How not to lose money… and human lives