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)
Part 1: Causality – an Introduction
Part 2: Causal Inference
Part 3: Causal Discovery

Step 2 – identifying the estimand(s)

This short section is all about finding estimands with DoWhy. We’ll start with a brief overview of estimands supported by the library and then jump straight into practice!

DoWhy offers three ways to find estimands:

  • Back-door
  • Front-door
  • Instrumental variable

We know all of them from the previous chapter. To see a quick practical introduction to all three methods, check out my blog post Causal Python — 3 Simple Techniques to Jump-Start Your Causal Inference Journey Today (Molak, 2022;

Let’s see how to use DoWhy in order to find a correct estimand for our model.

It turns out it is very easy! Just see for yourself:

estimand = model.identify_effect()

Yes, that’s all!

We just call the .identify_effect() method of our CausalModel object and we’re done!

Let’s print out our estimand to see what we can learn: