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

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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

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...

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

Table of Contents (21) Chapters

X-Learner – a step further

Squeezing the lemon

Authors (1)

Personalised recommendations for you

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

Table of Contents (21) Chapters

X-Learner – a step further

Squeezing the lemon

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you