You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803235424

Length 606 pages

Edition 2nd Edition

Languages

Python

Tools

BERT

Concepts

Machine Learning

Author (1):

Serg Masís

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Table of Contents (17) Chapters

Preface

1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter?

2. Key Concepts of Interpretability FREE CHAPTER

3. Interpretation Challenges

4. Global Model-Agnostic Interpretation Methods

5. Local Model-Agnostic Interpretation Methods

6. Anchors and Counterfactual Explanations

7. Visualizing Convolutional Neural Networks

8. Interpreting NLP Transformers

9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

10. Feature Selection and Engineering for Interpretability

11. Bias Mitigation and Causal Inference Methods

12. Monotonic Constraints and Model Tuning for Interpretability

13. Adversarial Robustness

14. What’s Next for Machine Learning Interpretability?

15. Other Books You May Enjoy

16. Index

Testing estimate robustness

The dowhy library comes with four methods to test the robustness of the estimated causal effect, outlined as follows:

Random common cause: Adding a randomly generated confounder. If the estimate is robust, the ATE should not change too much.
Placebo treatment refuter: Replacing treatments with random variables (placebos). If the estimate is robust, the ATE should be close to zero.
Data subset refuter: Removing a random subset of the data. If the estimator generalizes well, the ATE should not change too much.
Add unobserved common cause: Adding an unobserved confounder that is associated with both the treatment and outcome. The estimator assumes some level of unconfoundedness but adding more should bias the estimates. Depending on the strength of the confounder’s effect, it should have an equal impact on the ATE.

We will test robustness with the first two next.

Adding a random common cause

This method...

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You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Table of Contents (17) Chapters

Testing estimate robustness

Adding a random common cause

Authors (1)

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You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Table of Contents (17) Chapters

Testing estimate robustness

Adding a random common cause

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Authors (1)

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