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

Regression and structural models

Before we conclude this chapter, let’s take a look at the connection between regression and SCMs. You might already have an intuitive understanding that they are somehow related. In this section, we’ll discuss the nature of this relationship.

SCMs

In the previous chapter, we learned that SCMs are a useful tool for encoding causal models. They consist of a set of variables (exogenous and endogenous) and a set of functions defining the relationships between these variables. We saw that SCMs can be represented as graphs, with nodes representing variables and directed edges representing functions. Finally, we learned that SCMs can produce interventional and counterfactual distributions.

SCM and structural equations

In causal literature, the names structural equation model (SEM) and structural causal model (SCM) are sometimes used interchangeably (e.g., Peters et al., 2017). Others refer to SEMs as a family of specific multivariate...