Causal Inference and Discovery in Python: (Record no. 59954)
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| 000 -LEADER | |
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| fixed length control field | 04047 a2200229 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240319b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781804612989 (PB) |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 080 ## - UNIVERSAL DECIMAL CLASSIFICATION NUMBER | |
| Universal Decimal Classification number | 681.3 |
| Item number | MOL |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Molak, Aleksander |
| 245 ## - TITLE STATEMENT | |
| Title | Causal Inference and Discovery in Python: |
| Sub Title | Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Packt Publishing Limited |
| Year of publication | 2023 |
| Place of publication | Birmingham |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxv, 429p. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Table of ContentsCausality<br/>Hey, We Have Machine Learning, So Why Even Bother?Judea Pearl and the Ladder of CausationRegression, Observations, and InterventionsGraphical ModelsForks, Chains, and ImmoralitiesNodes, Edges, and Statistical (In)dependenceThe Four-Step Process of Causal InferenceCausal Models<br/>Assumptions and ChallengesCausal Inference and Machine Learning<br/>from Matching to Meta-LearnersCausal Inference and Machine Learning<br/>Advanced Estimators, Experiments, Evaluations, and MoreCausal Inference and Machine Learning<br/>Deep Learning, NLP, and BeyondCan I Have a Causal Graph, Please?Causal Discovery and Machine Learning<br/>from Assumptions to ApplicationsCausal Discovery and Machine Learning<br/>Advanced Deep Learning and BeyondEpilogue |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book DescriptionCausal 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. 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. What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Computer programs |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Python (Computer program language) |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | Computer Science |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Jaokar, Ajit |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | BOOKS |
| Withdrawn status | Lost status | Damaged status | Not for loan | Current library | Shelving location | Full call number | Accession Number | Koha item type |
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| IMSc Library | Second Floor, Rack No: 49, Shelf No: 11 | 681.3 MOL | 77603 | BOOKS |