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Causal Artificial Intelligence: The Next Step in Effective Business AI 1st Edition

3.7 out of 5 stars 25 ratings

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Discover the next major revolution in data science and AI and how it applies to your organization

In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.

Useful for both data scientists and business-side professionals, the book offers:

  • Clear and compelling descriptions of the concept of causality and how it can benefit your organization
  • Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems
  • Useful strategies for deciding when to use correlation-based approaches and when to use causal inference

An enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.

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Editorial Reviews

From the Back Cover

Explore the next major revolution in data science and artificial intelligence: causal AI

In Causal Artificial Intelligence: The Next Step in Effective Business AI, a team of distinguished AI and analytics professionals delivers an incisive and comprehensive exploration of the models and data of causal inference and causal artificial intelligence. Authors Judith Hurwitz and John Thompson offer the technical detail―explained clearly and accessibly―necessary to understand the underlying technologies, as well as the business context that frames causal AI from a perspective of daily business operations.

You’ll discover meaningful and practical insights into what causality is and how it can benefit your organization and understand the critical differences between correlation-based approaches to AI and causality-based approaches. The book also includes easy-to-understand use cases and examples that demonstrate the value of causality for solving business problems.

Perfect for data scientists, subject matter experts in a variety of fields, as well as managers, executives, and other business leaders, Causal Artificial Intelligence is a one-of-a-kind resource designed to open eyes and minds to the incredible possibilities of casual AI and its implications for businesses of all kinds.

About the Author

JUDITH S. HURWITZ is the chief evangelist at Geminos Software, a causal AI platform company. For more than 35 years she has been a strategist, technology consultant to software providers, and a thought leader having authored 10 books in topics ranging from augmented intelligence, data analytics, and cloud computing.

JOHN K. THOMPSON is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence (AI). John is responsible for the global AI function at EY. He has previously led the global Artificial Intelligence and Rapid Data Lab teams at CSL Behring and is the bestselling author of three books on data analytics.

Product details

  • Publisher ‏ : ‎ Wiley; 1st edition (October 3, 2023)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 384 pages
  • ISBN-10 ‏ : ‎ 1394184131
  • ISBN-13 ‏ : ‎ 978-1394184132
  • Item Weight ‏ : ‎ 7.4 ounces
  • Dimensions ‏ : ‎ 5.9 x 0.9 x 8.9 inches
  • Customer Reviews:
    3.7 out of 5 stars 25 ratings

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Customer reviews

3.7 out of 5 stars
25 global ratings

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5 out of 5 stars
Simple decision - Buy buy buy!
John and Judith knock it out of the park with this book. Much like “Building Analytics Teams,” this book speaks to the technical business professionals looking to sharpen their skills. Just today, I picked up this book to address a current modeling scenario, and it didn’t disappoint. Practical advise & easy reading, I buy these books sight unseen based on their consistent quality and expert insights.
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Top reviews from the United States

  • Reviewed in the United States on September 22, 2023
    Few authors – if any – have the ability to take highly technical innovations and breakthroughs in AI, and translate them into the “big picture”: What is it good for, how can I use it to improve my organization and its processes, and what should I do next? The authors of this book, Hurwitz and Thompson, are masters of this skill.

    As is widely known, AI is transformational, and critical for success in a competitive business environment, to optimize manufacturing processes, identify effective medical interventions, or just understand and interact with complex and challenging reality. UCLA Professor Judea Pearl’s recent work on causal AI has been recognized by experts as a breakthrough w/r to the application of Artificial Intelligence and Big-Data-Analytics, in order to understand and solve real-world problems. Professor Pearl was awarded the prestigious Turing Award for this work. Hurwitz and Thompson now deliver the key insights of Pearl’s leading-edge work to practitioners in this book.

    The fact that “correlation does not equal causation” is widely known and taught to be a key tenet of how to interpret and draw inferences from data. But: This is only part-of-the-story! In reality, in complex systems, effects can be direct or indirect, mediated, suppressed or augmented, reciprocal, etc. And modern analytic techniques can provide the tools to untangle the “knowledge graphs” to deliver insight into the causal relationships between measured and latent (non-observed, but inferred) variables and constructs. In Causal Artificial Intelligence, Hurwitz and Thompson provide an overview of the approach to Causal AI, explain critical terminology and definitions, how to go about retrieving the right info and data, where to find the right (e.g., Python) tools, and how to implement it. Practitioners will particularly appreciate the recipes and examples for success, based on the authors’ extensive real-world experience (and prior publications).

    So in sum, a must-read book for anyone who wants to stay on-top of critical developments in AI, and how they are rapidly changing all aspects of our world!

    (PS: Causal AI and Judea Pearl’s work is also briefly discussed in our recent book on Practical Data Analytics for Innovation in Medicine -- see: https://www.amazon.com/Practical-Data-Analytics-Innovation-Medicine. Hurwitz and Thompson’s book is a dedicated introduction for practitioners that I can highly recommend.)
    One person found this helpful
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  • Reviewed in the United States on August 4, 2024
    I was excited to read this book after an endorsement from the author of “The Book of Why”. It was more superficial than I hoped for and failed to work through real business examples to completion as the book went along.
  • Reviewed in the United States on February 8, 2024
    I found this book extremely good. As a long time product manager and marketing exec in the AI space, I learned a ton about the future of AI for business, and gained highly valuable insights into how business leaders should be looking at the era of digital reinvention driven by AI. Today's AI is very much in its infancy. While generative AI is top of mind, it will not be long before Causal AI becomes to focus area. It's one thing to understand WHAT is occurring, yet another to understand HOW, but the value is in the WHY. This book is written for both technologies and "mere mortal" business leaders to understand what to be pushing for in AI systems. The notion of "if this, then what", true AI explainability, cause and effect, etc are explored in the context of real use cases. The value of AI will ultimately evolve to an interactive experience to explore various approaches to problem solving and without causal inferencing, you can only do so much. I view this as a must read for c-suite leaders that are involved in driving business transformation involving AI. It will decode a ton of concepts and future applications AI. Finally, it's very well written book and easy to consume.
  • Reviewed in the United States on August 8, 2024
    John and Judith knock it out of the park with this book. Much like “Building Analytics Teams,” this book speaks to the technical business professionals looking to sharpen their skills.

    Just today, I picked up this book to address a current modeling scenario, and it didn’t disappoint.

    Practical advise & easy reading, I buy these books sight unseen based on their consistent quality and expert insights.
    Customer image
    Pat
    5.0 out of 5 stars
    Simple decision - Buy buy buy!

    Reviewed in the United States on August 8, 2024
    John and Judith knock it out of the park with this book. Much like “Building Analytics Teams,” this book speaks to the technical business professionals looking to sharpen their skills.

    Just today, I picked up this book to address a current modeling scenario, and it didn’t disappoint.

    Practical advise & easy reading, I buy these books sight unseen based on their consistent quality and expert insights.
    Images in this review
    Customer image
  • Reviewed in the United States on October 25, 2023
    Having picked up Judith Hurwitz and John Thompson's collaborative work "Causal Artificial Intelligence, The Next Step In Effective Business AI" from Amazon midweek, I expected to do a run of it over a weekend. The more time I spent with it, the more I wanted to slow down and think through the implications of a pivot from the correlation perspective to the causal one.

    The work offers a very readable and practical resource for anyone interested in the next phase of AI. It bridges the gap between theoretical understanding and practical application nicely, presenting a clear-eyed view of the domain of Causal AI. The authors' passion for the subject and commitment to ensuring the material is accessible make this book an essential addition to the library of data and AI professionals in leadership and practitioner roles. It is a good read for those who want to stay at the forefront of AI innovation and harness the potential of causal artificial intelligence.
    Customer image
    5.0 out of 5 stars
    Clear headed view on where we go next with Artificial Intelligence

    Reviewed in the United States on October 25, 2023
    Having picked up Judith Hurwitz and John Thompson's collaborative work "Causal Artificial Intelligence, The Next Step In Effective Business AI" from Amazon midweek, I expected to do a run of it over a weekend. The more time I spent with it, the more I wanted to slow down and think through the implications of a pivot from the correlation perspective to the causal one.

    The work offers a very readable and practical resource for anyone interested in the next phase of AI. It bridges the gap between theoretical understanding and practical application nicely, presenting a clear-eyed view of the domain of Causal AI. The authors' passion for the subject and commitment to ensuring the material is accessible make this book an essential addition to the library of data and AI professionals in leadership and practitioner roles. It is a good read for those who want to stay at the forefront of AI innovation and harness the potential of causal artificial intelligence.
    Images in this review
    Customer image
  • Reviewed in the United States on April 12, 2024
    Unfortutanely the book is a shallow and superficial attempt, it keeps making the same pitches over and over.
  • Reviewed in the United States on November 24, 2023
    I highly recommend “Causal Artificial Intelligence” by Hurwitz and Thompson as a must-read for both AI practitioners and, perhaps more importantly, business leaders seeking deeper insights into driving successful AI projects. Cause AI emphasizes the importance of understanding the principles of causation (not just correlation) in real world AI scenarios by forming hybrid teams with data experts, business leaders, and subject matter experts. The authors demystify complex AI concepts to ensure that both seasoned AI professionals and business leaders can understand the concepts and harness the power of Causal AI. As a business executive I found the book at about the right level and very helpful in terms of providing specific guidance on different approaches to successful projects.
    Customer image
    5.0 out of 5 stars
    A must-read book for business leaders seeking deeper insights into driving successful AI projects

    Reviewed in the United States on November 24, 2023
    I highly recommend “Causal Artificial Intelligence” by Hurwitz and Thompson as a must-read for both AI practitioners and, perhaps more importantly, business leaders seeking deeper insights into driving successful AI projects. Cause AI emphasizes the importance of understanding the principles of causation (not just correlation) in real world AI scenarios by forming hybrid teams with data experts, business leaders, and subject matter experts. The authors demystify complex AI concepts to ensure that both seasoned AI professionals and business leaders can understand the concepts and harness the power of Causal AI. As a business executive I found the book at about the right level and very helpful in terms of providing specific guidance on different approaches to successful projects.
    Images in this review
    Customer image