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

Part 3: Causal Discovery

In Part 3, we will start our journey into the world of causal discovery. We will begin with an overview of the sources of causal knowledge and a deeper look at important assumptions.

We will introduce four families of causal discovery algorithms and implement them using gCastle. We will move toward advanced methods and demonstrate how to train a DECI algorithm using PyTorch.

Along the way, we will show you how to inject expert knowledge into the causal discovery process, and we will briefly discuss methods that allow us to combine observational and interventional data to learn causal structure more efficiently.

We will close Part 3 with a summary of the book, a discussion of causality in business, a sneak peek into the (potential) future of the field, and pointers to more resources on causal inference and discovery for those who are ready to continue their causal journey.

This part comprises the following chapters: