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

Wrapping it up

Congrats on finishing Chapter 9!

We presented a lot of information in this chapter! Let’s summarize!

We started with the basics and introduced the matching estimator. On the way, we defined ATE, ATT, and ATC.

Then, we moved to propensity scores. We learned that propensity score is the probability of being treated, which we compute for each observation. Next, we’ve shown that although it might be tempting to use propensity scores for matching, in reality, it’s a risky idea. We said that propensity scores can shine in other scenarios, and we introduced propensity score weighting, which allows us to construct sub-populations and weight them accordingly in order to deconfound our data (it does not help when we have unobserved confounding).

Next, we started our journey with meta-learners. We said that ATE can sometimes hide important information from us and we defined CATE. This opened the door for us to explore the world of HTEs, where units...