Causal Ai
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Causal Ai
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Author : Ajit Singh
language : en
Publisher: Independently Published
Release Date : 2025-09-12
Causal Ai written by Ajit Singh and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-12 with Computers categories.
"Causal AI: Beyond Correlation" is a comprehensive, practical, and accessible guide to the principles and practices of Causal Inference and its application in modern Artificial Intelligence. Designed for B.Tech and M.Tech students in Computer Science, Data Science, and related engineering disciplines, this book serves as both a foundational textbook and a hands-on manual for building more intelligent, robust, and interpretable AI systems. Key Features of This Book: 1. Beginner to Advanced Trajectory: The book follows a logical progression, starting with the fundamental concepts of causality and gradually building up to advanced topics like Causal Machine Learning, Counterfactuals, and Deep Learning integrations. 2. Practical, Hands-On Approach: Every chapter includes hands-on labs and coding exercises in Python, using popular libraries like DoWhy and EconML. Readers don't just learn theory; they apply it. 3. Real-World Case Studies: The book is rich with case studies from various domains, such as evaluating marketing campaign effectiveness, assessing the impact of a new medical treatment, and building fair and unbiased algorithms. 4. Complete Capstone Project: The final chapter guides the reader step-by-step through a live, end-to-end Causal AI project, including data preprocessing, model building, causal analysis, and interpretation of results, complete with fully explained code. 5. Clarity and Simplicity: Complex mathematical ideas are broken down into simple, intuitive explanations, often supported by visual aids and analogies, making the subject accessible to a broad audience. 6. Focus on a Foundational Skill: This book teaches a timeless and tool-agnostic skill-causal reasoning. This skill will remain valuable regardless of how AI frameworks and technologies evolve. For B.Tech and M.Tech students, who will be the architects of tomorrow's technological landscape, a deep understanding of causality is no longer optional-it is essential. Whether you are building economic models, designing clinical trials, optimizing supply chains, or creating fair and unbiased algorithms, the principles in this book will provide you with a powerful and indispensable toolkit.
Causal Ai
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Author : Robert Osazuwa Ness
language : en
Publisher: Simon and Schuster
Release Date : 2025-03-18
Causal Ai written by Robert Osazuwa Ness and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Computers categories.
Causal AI is a practical introduction to building AI models that can reason about causality. Robert Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.
Causal Artificial Intelligence
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Author : Judith S. Hurwitz
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-23
Causal Artificial Intelligence written by Judith S. Hurwitz and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-23 with Computers categories.
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.
Causality For Artificial Intelligence
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Author : Jordi Vallverdú
language : en
Publisher: Springer Nature
Release Date : 2024-06-28
Causality For Artificial Intelligence written by Jordi Vallverdú and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Computers categories.
How can we teach machine learning to identify causal patterns in data? This book explores the very notion of “causality”, identifying from a naturalistic and evolutionary perspective how living systems deal with causal relationships. At the same time, using this knowledge to identify the best ways to apply such biological models in machine learning scenarios. One of the more fundamental challenges for AI experts is to design machines that can understand the world, identifying the basic rules that govern reality. Statistics are powerful and fundamental for this process, but they are only one of the necessary tools. Counterfactual thinking is the other part of the necessary process that will help machines to become intelligent. This book explains the paths that can lead to algorithmic causality. It is essential reading for those who are not afraid of thinking at the interface of various academic disciplines or fields (AI, machine learning, philosophy, neuroscience, anthropology, psychology, computer sciences), and who are interested in the analysis of causal thinking and the ways in which cognitive systems (natural or artificial) can act in order to understand their environment. Professor Vallverdú is currently working on biomimetic cognitive architectures and multicognitive systems. His research has explored two main areas: epistemology and cognition. Since his early Ph.D. research on epistemic controversies, he has analyzed several aspects of computational epistemology. His latest research has focused on the causal challenges of machine learning techniques, particularly deep learning. One of his most promising advances is statistics meets causal graph reasoning (via Directed Acyclic Graphs), which still has several conceptual paths that need to be explored and identified. Counterfactual reasoning is a fundamental part of these open debates, which are under the analysis of Prof. Vallverdú. His current research is supported as part of the following projects: GEHUCT and ICREA Acadèmia.
Neutrosophic Sets And Systems Vol 84 2025
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Author : Florentin Smarandache
language : en
Publisher: Infinite Study
Release Date :
Neutrosophic Sets And Systems Vol 84 2025 written by Florentin Smarandache and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
This special issue of “Neutrosophic Sets and Systems”, a compilation of a workshop held at the Universidad Tecnológica de El Salvador, explores the application of neutrosophic frameworks in a dialogue with Latin American worldviews to address contemporary challenges. The volume challenges traditional Western logic, which often fails to capture the complexity and ambiguity of real-world contexts, particularly in Latin America. It highlights how neutrosophy's embrace of indeterminacy and contradiction can provide a more flexible and contextualized understanding. The papers presented bridge neutrosophic concepts with indigenous knowledge systems, such as Amerindian perspectivism, which already transcend classical dualisms. The research covers diverse topics including the use of neutrosophy in medical decision-making, trend identification in scientific articles via natural language processing, and the evaluation of sustainable projects that integrate ancestral knowledge into the circular economy. The issue demonstrates the contributions of Latin American thinkers to non-classical logic and showcases neutrosophy's potential to provide philosophical and practical solutions to social problems.
Information Systems Research In Vietnam Volume 3
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Author : Nguyen Hoang Thuan
language : en
Publisher: Springer Nature
Release Date : 2025-01-25
Information Systems Research In Vietnam Volume 3 written by Nguyen Hoang Thuan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-25 with Computers categories.
This book, the third volume of Information Systems Research in Vietnam, presents a special theme that focuses on two emerging and critical topics of the twenty-first century: “Digital Transformation” and “Sustainable Development”. Digital transformation, which consists of digitization of products and digitalization of work processes, has brought forth exciting new business models that disrupt traditional industries. Digital transformation has been embedded in the 2030 National Digital Transformation Programme of the Vietnamese government, leading to numerous digital businesses that offer significant value in various sectors, including retail, manufacturing, education, and health care. Partly due to the United Nations (UN) 17 Sustainable Development Goals (SDGs) specifying the key development areas and outlining collective actions to ensure continuing peace and prosperity for people and the planet, organizations in Vietnam are becoming increasingly aware of the importance of sustainable development and the adoption of sustainability governance frameworks such as ESG to gain strategic advantages in the turbulent markets. Information systems (IS), in particular, has profound impacts on achieving Sustainable Development. However, best practices and case studies about Digital Transformation and how this transformation and IS applications influence Sustainable Development in Vietnam have not been documented and studied, in spite of the rapid developments in these areas in both public and private sectors. This book, therefore, contributes to the existing body of knowledge and benefits a wide range of readers in several ways. Firstly, the book benefits scholars and students, both in Vietnam and globally, by advancing knowledge and presenting research on the latest trends in contemporary topics such as Digital Transformation and Sustainable Development, especially in the under-researched Vietnam context. Secondly, industry practitioners and experts, both in Vietnam and globally, will benefit from reading this book to keep up with the current trends, case studies, and applications. Thirdly, by presenting the most up to date knowledge on the topic, this book creates a shared understanding to help facilitate future research in the IS field, as well as providing the background to pave the way for collaboration between scholars, experts, and industry practitioners.
Causal Ai And Its Applications
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Author : Ajit Singh
language : en
Publisher: Independently Published
Release Date : 2025-08-03
Causal Ai And Its Applications written by Ajit Singh and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-03 with Computers categories.
This book, "Causal AI and Its Applications," is born out of the necessity to bridge this gap. It is an invitation to journey beyond correlation and into the world of causation. Causal AI is not just another subfield of machine learning; it is a paradigm shift that reorients our focus from mere prediction to deep understanding, from passive observation to active intervention. It is the science of asking "what if?" questions and getting principled, data-driven answers. What if we change our marketing strategy? What if we approve a new medical treatment? What if we implement a new economic policy? Answering these questions is impossible without a causal framework. Key Features: 1. Practical, Hands-on Approach: Every theoretical concept is paired with a Hands-on Lab section, featuring Python code, popular libraries (DoWhy, Causal-Learn, CausalNex), and simple datasets to ensure you learn by doing. 2. End-to-End Capstone Project: The final chapter is a complete, working capstone project that guides you through solving a real-world problem-from defining the causal question to implementing the code and interpreting the results for stakeholders. 3. Clear Theoretical Foundations: Complex topics like Structural Causal Models (SCMs) and the do-calculus are demystified with simple language, intuitive diagrams, and step-by-step examples. 4. Real-World Case Studies: Each application chapter includes detailed case studies that show how Causal AI is used at companies and research institutions to solve high-impact problems in marketing, finance, medicine, and policy-making. 5. Updated and Relevant Content: The book covers the latest advancements in the field, including the intersection of Causal AI with modern machine learning topics like fairness, explainability (XAI), and reinforcement learning. 6. Accessible for All: Written for students and practitioners, the book requires only a basic understanding of probability and Python, making it accessible to a broad audience. 7. By the end of this book, you will not just be a user of AI tools; you will be a scientific thinker capable of building more robust, ethical, and intelligent systems that can reason about the world in a fundamentally deeper way. This book addresses a critical need in modern data science and AI education. While most curricula focus on predictive modeling, this text champions a new way of thinking-causal reasoning. It provides a structured journey from the fundamental philosophy of causation to the practical application of cutting-edge algorithms for discovering causal relationships and estimating the impact of interventions.
Artificial Intelligence And Causal Inference
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Author : Momiao Xiong
language : en
Publisher: CRC Press
Release Date : 2022-02-03
Artificial Intelligence And Causal Inference written by Momiao Xiong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-03 with Business & Economics categories.
Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features: Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training. Deep learning for nonlinear mediation and instrumental variable causal analysis. Construction of causal networks is formulated as a continuous optimization problem. Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks. Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes. AI-based methods for estimation of individualized treatment effect in the presence of network interference.
Causal Ai For Data Scientists
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Author : Nurul Hakim Asif
language : en
Publisher:
Release Date : 2025-11-07
Causal Ai For Data Scientists written by Nurul Hakim Asif and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-07 with Computers categories.
Causal AI for Data Scientists: A Framework to Automate Discovery of Causal Relationships in Noisy Datasets presents a significant advancement in the intersection of machine learning, statistical inference, and mechanistic interpretability, tackling the long-standing challenge of uncovering robust causal structures in the presence of noise, latent confounders, and high-dimensional data. The paper is a timely and impactful contribution that addresses the core limitations of existing causal discovery algorithms, many of which falter under real-world conditions due to restrictive assumptions or an over-reliance on statistical correlations. Traditional approaches like Granger causality, PC algorithm, GES, and even more recent neural methods like NOTEARS and DAG-GNN have made strides in structure discovery but often fail in practical settings where data is incomplete, entangled, and affected by hidden variables. In response to these challenges, the authors propose a hybrid, multi-level causal inference framework that blends symbolic methods and neural network-based architectures. This fusion of symbolic reasoning and neural computation is designed to automate the discovery of causal relationships by applying targeted interventions and leveraging latent representations that encode abstract but interpretable features. The framework is grounded in an encoder-decoder neural network architecture that performs layered interventions at three distinct levels-input, latent, and hidden layers-thereby enabling a comprehensive examination of how information and influence propagate through the system. At the heart of this approach is a suite of novel causal metrics, including Causal Effect Strength (CES), Intervention Specificity (IS), and Polysemanticity Score (PS). CES quantifies the magnitude of influence one variable exerts on another when perturbed, while IS captures the precision or localization of this influence, distinguishing meaningful causal drivers from confounded or noisy variables.
Ai Ia
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Author : Associazione italiana per l'intelligenza artificiale. Congress
language : en
Publisher:
Release Date : 2001
Ai Ia written by Associazione italiana per l'intelligenza artificiale. Congress and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Artificial intelligence categories.