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

Inverse probability weighting (IPW)

In this section, we’ll discuss IPW. We’ll see how IPW can be used to de-bias our causal estimates, and we’ll implement it using DoWhy.

Many faces of propensity scores

Although propensity scores might not be the best choice for matching, they still might be useful in other contexts. IPW is a method that allows us to control for confounding by creating so-called pseudo-populations within our data. Pseudo-populations are created by upweighting the underrepresented and downweighting the overrepresented groups in our dataset.

Imagine that we want to estimate the effect of drug D. If males and females react differently to D and we have 2 males and 6 females in the treatment group and 12 males and 2 females in the control group, we might end up with a situation similar to the one that we’ve seen in Chapter 1: the drug is good for everyone, but is harmful to females and males!

This is Simpson’s paradox at its...