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

T-Learner – together we can do more

In this section, we’ll learn what T-Learner is and how it’s different from S-Learner. We’ll implement the model using DoWhy and EconML and compare its performance with the model from the previous section. Finally, we’ll discuss some of the drawbacks of T-Learner before concluding the section.

Forcing the split on treatment

The basic motivation behind T-Learner is to overcome the main limitation of S-Learner. If S-Learner can learn to ignore the treatment, why not make it impossible to ignore the treatment?

This is precisely what T-Learner is. Instead of fitting one model on all observations (treated and untreated), we now fit two models – one only on the treated units, and the other one only on the untreated units.

In a sense, this is equivalent to forcing the first split in a tree-based model to be a split on the treatment variable. Figure 9.12 presents a visual presentation of this concept:

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