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

Causal Forests and more

In this short section, we’ll provide a brief overview of the idea behind Causal Forests. We’ll introduce one of the EconML classes implementing the method. An in-depth discussion on Causal Forests and their extensions is beyond the scope of this book, but we’ll point to resources where you can learn more about forest-based causal estimators.

Causal Forest is a tree-based model that stems from the works of Susan Athey, Julie Tibshirani, and Stefan Wager (Wager & Athey, 2018; Athey et al., 2019). The core difference between regular random forest and Causal Forest is that Causal Forest uses so-called causal trees. Otherwise, the methods are similar and both use resampling, predictor subsetting, and averaging over a number of trees.

Causal trees

What makes causal trees different from regular trees is the split criterion. Causal trees use a criterion based on the estimated treatment effects, using so-called honest splitting, where...