You're reading from Causal Inference and Discovery in Python

Product type Book

Published in May 2023

Publisher Packt

ISBN-13 9781804612989

Pages 456 pages

Edition 1st Edition

Languages

Concepts

Data Science

Author (1):

Aleksander Molak

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?

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

X-Learner – a step further

In this section, we’ll introduce X-Learner – a meta-learner built to make better use of the information available in the data. We’ll learn how X-Learner works and implement the model using our familiar DoWhy pipeline.

Finally, we’ll compute the effect estimates on the full earnings dataset and compare the results with S- and T-Learners. We’ll close this section with a set of recommendations on when using X-Learner can be beneficial and a summary of all three sections about meta-learners.

Let’s start!

Squeezing the lemon

Have you noticed something?

Every time we built a meta-learner so far, we estimated two potential outcomes separately (using a single model in the case of S-Learner, and two models in the case of T-Learner) and then subtracted them in order to obtain CATE.

In a sense, we never tried to use our estimators to actually estimate CATE. We were rather estimating both potential outcomes...