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

Step 1 – modeling the problem

In this section, we’ll discuss and practice step 1 of the four-step causal inference process: modeling the problem.

We’ll split this step into two substeps:

  1. Creating a graph representing our problem
  2. Instantiating DoWhy’s CausalModel object using this graph

Creating the graph

In Chapter 3, we introduced a graph language called GML. We’ll use GML to define our data-generating process in this section.

Figure 7.1 presents the GPS example from the previous chapter, which we’ll model next. Note that we have omitted variable-specific noise for clarity:

Figure 7.1 – The graphical model from Chapter 6

Figure 7.1 – The graphical model from Chapter 6

Note that the graph in Figure 7.1 contains an unobserved variable, U. We did not include this variable in our dataset (it’s unobserved!), but we’ll include it in our graph. This will allow DoWhy to recognize that there’s an unobserved confounder...