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

Graphs, graphs, graphs

This section will be a quick refresher on graphs and basic graph theory. If you’re not familiar with graphs – don’t worry – you can treat this section as a crash course on the topic.

Let’s start!

Graphs can be defined in multiple ways. You can think of them as discrete mathematical structures, abstract representations of real-world entities and relations between them, or computational data structures. What all of these perspectives have in common are the basic building blocks of graphs: nodes (also called vertices) and edges (links) that connect the nodes.

Types of graphs

We can divide graphs into types based on several attributes. Let’s discuss the ones that are the most relevant from the causal point of view.

Undirected versus directed

Directed graphs are graphs with directed edges, while undirected graphs have undirected edges. Figure 4.1 presents an example of a directed and undirected graph:

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