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)
1
Part 1: Causality – an Introduction
7
Part 2: Causal Inference
14
Part 3: Causal Discovery

Learning causality

In this section, we’ll point to the resources to learn more about causality after finishing this book.

For many people starting with causality, their learning path begins with excitement. The promise of causality is attractive and powerful. After learning about the basics and realizing the challenges that any student of causality has to face, many of us lose hope in the early stages of our journeys.

Some of us regain it, learning that solutions do exist, although not necessarily where we initially expected to find them.

After overcoming the first challenges and going deeper into the topic, many of us realize that there are more difficulties to come. Learning from earlier experiences, it’s easier at this stage to realize that (many of) these difficulties can be tackled using a creative and systematic approach.

I like the way the Swiss educator and researcher Quentin Gallea presented the journey into learning causality in a graphical form...