Causal Artificial Intelligence
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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.
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.
Elements Of Causal Inference
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Author : Jonas Peters
language : en
Publisher: MIT Press
Release Date : 2017-12-29
Elements Of Causal Inference written by Jonas Peters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-29 with Computers categories.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
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.
Machine Learning For Causal Inference
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Author : Sheng Li
language : en
Publisher: Springer Nature
Release Date : 2023-11-25
Machine Learning For Causal Inference written by Sheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-25 with Computers categories.
This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
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.
Causality Correlation And Artificial Intelligence For Rational Decision Making
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Author : Tshilidzi Marwala
language : en
Publisher: World Scientific
Release Date : 2015-01-02
Causality Correlation And Artificial Intelligence For Rational Decision Making written by Tshilidzi Marwala and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-02 with Computers categories.
Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman-Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The concept of rational decision making is studied, and the theory of flexibly-bounded rationality is used to extend the theory of bounded rationality within the principle of the indivisibility of rationality. The theory of the marginalization of irrationality for decision making is also introduced to deal with satisficing within irrational conditions. The methods proposed are applied in biomedical engineering, condition monitoring and for modelling interstate conflict.
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.
Causal Models And Intelligent Data Management
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Author : Alex Gammerman
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Causal Models And Intelligent Data Management written by Alex Gammerman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods. This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.