MARC details
| 000 -LEADER |
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| fixed length control field |
02811nam a22002537a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250829020003.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240731b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781804612989 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
IIITD |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
MOL-C |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Molak, Aleksander |
| 245 ## - TITLE STATEMENT |
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| Title |
Causal inference and discovery in python : |
| Remainder of title |
unlock the secrets of modern causal machine learning with dowhy, econML, pytorch and more |
| Statement of responsibility, etc |
by Aleksander Molak |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
England : |
| Name of publisher, distributor, etc |
Packt Publishing, |
| Date of publication, distribution, etc |
©2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxv, 429 p. : |
| Other physical details |
ill. ; |
| Dimensions |
26 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
1. Causality Hey, We Have Machine Learning, So Why Even Bother? |
| -- |
2. Judea Pearl and the Ladder of Causation |
| -- |
3. Regression, Observations, and Interventions |
| -- |
4. Graphical Models |
| -- |
5. Forks, Chains, and Immoralities |
| -- |
6. Nodes, Edges, and Statistical (In)dependence |
| -- |
7. The Four-Step Process of Causal Inference |
| -- |
8. Causal Models Assumptions and Challenges |
| -- |
9. Causal Inference and Machine Learning from Matching to Meta-Learners |
| -- |
10. Causal Inference and Machine Learning Advanced Estimators, Experiments, Evaluations, and More |
| -- |
11. Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond |
| -- |
12. Can I Have a Causal Graph, Please? |
| -- |
13. Causal Discovery and Machine Learning - from Assumptions to Applications |
| -- |
14. Causal Discovery and Machine Learning - Advanced Deep Learning and Beyond |
| -- |
15. Epilogue |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.<br/><br/>You'll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you'll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you'll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You'll further explore the mechanics of how "causes leave traces" and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Aprenentatge automàtic. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Llenguatge de programació) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha issues (borrowed), all copies |
4 |
