You're reading from Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781804612989

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Author (1):

Aleksander Molak

View More author details

Table of Contents (21) Chapters

Preface

1. Part 1: Causality – an Introduction

2. Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? FREE CHAPTER

3. Chapter 2: Judea Pearl and the Ladder of Causation

4. Chapter 3: Regression, Observations, and Interventions

5. Chapter 4: Graphical Models

6. Chapter 5: Forks, Chains, and Immoralities

7. Part 2: Causal Inference

8. Chapter 6: Nodes, Edges, and Statistical (In)dependence

9. Chapter 7: The Four-Step Process of Causal Inference

10. Chapter 8: Causal Models – Assumptions and Challenges

11. Chapter 9: Causal Inference and Machine Learning – from Matching to Meta-Learners

12. Chapter 10: Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More

13. Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond

14. Part 3: Causal Discovery

15. Chapter 12: Can I Have a Causal Graph, Please?

16. Chapter 13: Causal Discovery and Machine Learning – from Assumptions to Applications

17. Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond

18. Chapter 15: Epilogue

19. Index

20. Other Books You May Enjoy

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:

Creating a graph representing our problem
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

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...