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

From associations to logic and imagination – the Ladder of Causation

In this section, we’ll introduce the concept of the Ladder of Causation and summarize its building blocks. Figure 2.1 presents a symbolic representation of the Ladder of Causation. The higher the rung, the more sophisticated our capabilities become, but let’s start from the beginning:

Figure 2.1 – The Ladder of Causation. Image by the author, based on a picture by Laurie Shaw (https://www.pexels.com/photo/brown-wooden-door-frame-804394/)

Figure 2.1 – The Ladder of Causation. Image by the author, based on a picture by Laurie Shaw (https://www.pexels.com/photo/brown-wooden-door-frame-804394/)

The Ladder of Causation, introduced by Judea Pearl (Pearl, Mackenzie, 2019), is a helpful metaphor for understanding distinct levels of relationships between variables – from simple associations to counterfactual reasoning. Pearl’s ladder has three rungs. Each rung is related to different activity and offers answers to different types of causal questions. Each rung comes with a distinct set of mathematical tools...