{"product_id":"causal-artificial-intelligence-isbn-9781394184132","title":"Causal Artificial Intelligence","description":"\u003cp\u003e\u003cb\u003eDiscover the next major revolution in data science and AI and how it applies to your organization\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eCausal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eUseful for both data scientists and business-side professionals, the book offers: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eClear and compelling descriptions of the concept of causality and how it can benefit your organization\u003c\/li\u003e \u003cli\u003eDetailed use cases and examples that vividly demonstrate the value of causality for solving business problems\u003c\/li\u003e \u003cli\u003eUseful strategies for deciding when to use correlation-based approaches and when to use causal inference\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eAn enlightening and easy-to-understand treatment of an essential business topic, \u003ci\u003eCausal Artificial Intelligence\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003eIntroduction xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Setting the Stage for Causal AI 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy Causality Is a Game Changer 2\u003c\/p\u003e \u003cp\u003eCausal AI in Perspective with Analytics 7\u003c\/p\u003e \u003cp\u003eAnalytical Sophistication Model 8\u003c\/p\u003e \u003cp\u003eAnalytics Enablers 10\u003c\/p\u003e \u003cp\u003eAnalytics 10\u003c\/p\u003e \u003cp\u003eAdvanced Analytics 11\u003c\/p\u003e \u003cp\u003eScope of Services to Support Causal AI 11\u003c\/p\u003e \u003cp\u003eThe Value of the Hybrid Team 13\u003c\/p\u003e \u003cp\u003eThe Promise of AI 14\u003c\/p\u003e \u003cp\u003eUnderstanding the Core Concepts of Causal AI 15\u003c\/p\u003e \u003cp\u003eExplainability and Bias Detection 15\u003c\/p\u003e \u003cp\u003eExplainability 17\u003c\/p\u003e \u003cp\u003eDetecting Bias in a Model 17\u003c\/p\u003e \u003cp\u003eDirected Acyclic Graphs 18\u003c\/p\u003e \u003cp\u003eStructural Causal Model 19\u003c\/p\u003e \u003cp\u003eObserved and Unobserved Variables 20\u003c\/p\u003e \u003cp\u003eCounterfactuals 21\u003c\/p\u003e \u003cp\u003eConfounders 21\u003c\/p\u003e \u003cp\u003eColliders 22\u003c\/p\u003e \u003cp\u003eFront- Door and Backdoor Paths 23\u003c\/p\u003e \u003cp\u003eCorrelation 24\u003c\/p\u003e \u003cp\u003eCausal Libraries and Tools 25\u003c\/p\u003e \u003cp\u003ePropensity Score 25\u003c\/p\u003e \u003cp\u003eAugmented Intelligence and Causal AI 26\u003c\/p\u003e \u003cp\u003eSummary 27\u003c\/p\u003e \u003cp\u003eNote 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Understanding the Value of Causal AI 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Causal AI 30\u003c\/p\u003e \u003cp\u003eThe Origins of Causal AI 33\u003c\/p\u003e \u003cp\u003eWhy Causality? 34\u003c\/p\u003e \u003cp\u003eExpressing Relationships 37\u003c\/p\u003e \u003cp\u003eThe Ladder of Causation 38\u003c\/p\u003e \u003cp\u003eRung 1: Association, or Passive Observation 40\u003c\/p\u003e \u003cp\u003eRung 2: Intervention, or Taking Action 40\u003c\/p\u003e \u003cp\u003eRung 3: Counterfactuals, or Imagining What If 42\u003c\/p\u003e \u003cp\u003eWhy Causal AI Is the Next Generation of AI 43\u003c\/p\u003e \u003cp\u003eDeep Learning and Neural Networks 43\u003c\/p\u003e \u003cp\u003eNeural Networks 44\u003c\/p\u003e \u003cp\u003eEstablishing Ground Truth 45\u003c\/p\u003e \u003cp\u003eThe Business Imperative of a Causal Model 46\u003c\/p\u003e \u003cp\u003eThe Importance of Augmented Intelligence 51\u003c\/p\u003e \u003cp\u003eThe Importance of Data, Visualization, and Frameworks 52\u003c\/p\u003e \u003cp\u003eGetting the Appropriate Data 52\u003c\/p\u003e \u003cp\u003eApplying Data and Model Visualization 55\u003c\/p\u003e \u003cp\u003eApplying Frameworks After Creating a Model 56\u003c\/p\u003e \u003cp\u003eGetting Started with Causal AI 57\u003c\/p\u003e \u003cp\u003eSummary 58\u003c\/p\u003e \u003cp\u003eNotes 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Elements of Causal AI 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConceptual Models 62\u003c\/p\u003e \u003cp\u003eCorrelation vs. Causal Models 63\u003c\/p\u003e \u003cp\u003eCorrelation- Based AI 63\u003c\/p\u003e \u003cp\u003eCausal AI 63\u003c\/p\u003e \u003cp\u003eUnderstanding the Relationship Between Correlation and Causality 64\u003c\/p\u003e \u003cp\u003eProcess Models 66\u003c\/p\u003e \u003cp\u003eCorrelation- Based AI Process Model 67\u003c\/p\u003e \u003cp\u003eCausal- Based AI Process Model 69\u003c\/p\u003e \u003cp\u003eCollaboration Between Business and Analytics Professionals 72\u003c\/p\u003e \u003cp\u003eThe Fundamental Building Blocks of Causal AI Models 75\u003c\/p\u003e \u003cp\u003eThe Relations Between DAGs and SCMs 76\u003c\/p\u003e \u003cp\u003eExplaining DAGs 76\u003c\/p\u003e \u003cp\u003eCausal Notation: The Language of DAGs 78\u003c\/p\u003e \u003cp\u003eOperationalizing a DAG with an SCM 79\u003c\/p\u003e \u003cp\u003eThe Elements of Visual Modeling 81\u003c\/p\u003e \u003cp\u003eNodes 83\u003c\/p\u003e \u003cp\u003eVariables 83\u003c\/p\u003e \u003cp\u003eEndogenous and Exogenous Variables 83\u003c\/p\u003e \u003cp\u003eObserved and Unobserved Variables 84\u003c\/p\u003e \u003cp\u003ePaths\/Relationships 84\u003c\/p\u003e \u003cp\u003eWeights 86\u003c\/p\u003e \u003cp\u003eSummary 88\u003c\/p\u003e \u003cp\u003eNotes 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Creating Practical Causal AI Models and Systems 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Complex Models 92\u003c\/p\u003e \u003cp\u003eCausal Modeling Process: Part 1 94\u003c\/p\u003e \u003cp\u003eStep 1: What Are the Intended Outcomes? 95\u003c\/p\u003e \u003cp\u003eStep 2: What Are the Proposed Interventions? 97\u003c\/p\u003e \u003cp\u003eStep 3: What Are the Confounding Factors? 99\u003c\/p\u003e \u003cp\u003eStep 4: What Are the Factors Creating the Effects and Changes? 102\u003c\/p\u003e \u003cp\u003eCommon\/Universal Effects in a Causal Model 102\u003c\/p\u003e \u003cp\u003eRefined Effects in a Causal Model 103\u003c\/p\u003e \u003cp\u003eStep 5: Creating a Directed Acyclic Graph 105\u003c\/p\u003e \u003cp\u003eStep 6: Paths and Relationships 105\u003c\/p\u003e \u003cp\u003eTypes of Paths 106\u003c\/p\u003e \u003cp\u003ePath Connecting an Unobserved Variable 107\u003c\/p\u003e \u003cp\u003eFront- Door Paths 108\u003c\/p\u003e \u003cp\u003eBackdoor Paths 108\u003c\/p\u003e \u003cp\u003eModeling for Simplicity to Understand Complexity 109\u003c\/p\u003e \u003cp\u003eStep 7: Data Acquisition 110\u003c\/p\u003e \u003cp\u003eCausal- Based Approach: Part 2 112\u003c\/p\u003e \u003cp\u003eStep 8: Data Integration 113\u003c\/p\u003e \u003cp\u003eStep 9: Model Modification 114\u003c\/p\u003e \u003cp\u003eStep 10: Data Transformation 115\u003c\/p\u003e \u003cp\u003eStep 11: Preparing for Deployment in Business 118\u003c\/p\u003e \u003cp\u003eSummary 121\u003c\/p\u003e \u003cp\u003eNotes 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Creating a Model with a Hybrid Team 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Hybrid Team 126\u003c\/p\u003e \u003cp\u003eWhy a Hybrid Team? 127\u003c\/p\u003e \u003cp\u003eThe Benefits of a Hybrid Team 128\u003c\/p\u003e \u003cp\u003eEstablishing the Hybrid Team as a Center of Excellence 129\u003c\/p\u003e \u003cp\u003eHow Teams Collaborate 131\u003c\/p\u003e \u003cp\u003eBut Why? 132\u003c\/p\u003e \u003cp\u003eDefining Roles 134\u003c\/p\u003e \u003cp\u003eLeaders and Business Strategists 137\u003c\/p\u003e \u003cp\u003eSubject- Matter Experts 138\u003c\/p\u003e \u003cp\u003eData Experts 140\u003c\/p\u003e \u003cp\u003eSoftware Developers 142\u003c\/p\u003e \u003cp\u003eBusiness Process Analysts 143\u003c\/p\u003e \u003cp\u003eInformation Technology Expertise 143\u003c\/p\u003e \u003cp\u003eProject Manager(s) 144\u003c\/p\u003e \u003cp\u003eThe Basics Steps for a Hybrid Team Project 145\u003c\/p\u003e \u003cp\u003eAn Overview of Model Creation 146\u003c\/p\u003e \u003cp\u003eIt Depends on Your Destination 150\u003c\/p\u003e \u003cp\u003eUnderstanding the Root Cause of a Problem 151\u003c\/p\u003e \u003cp\u003eUnderstanding What Happened and Why 153\u003c\/p\u003e \u003cp\u003eThe Importance of the Iterative Process 154\u003c\/p\u003e \u003cp\u003eSummary 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Explainability, Bias Detection, and AI Responsibility in Causal AI 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExplainability 158\u003c\/p\u003e \u003cp\u003eThe Ramifications of the Lack of Explainability 159\u003c\/p\u003e \u003cp\u003eWhat Is Explainable AI in Causal AI Models? 161\u003c\/p\u003e \u003cp\u003eBlack Boxes 162\u003c\/p\u003e \u003cp\u003eInternal Workings of Black-Box Models 162\u003c\/p\u003e \u003cp\u003eDeep Learning at the Heart of Black Boxes 163\u003c\/p\u003e \u003cp\u003eIs Code Understandable? 163\u003c\/p\u003e \u003cp\u003eThe Value of White-Box Models 166\u003c\/p\u003e \u003cp\u003eUnderstanding Causal AI Code 167\u003c\/p\u003e \u003cp\u003eTechniques for Achieving Explainability 169\u003c\/p\u003e \u003cp\u003eChallenges of Complex Causal Models 169\u003c\/p\u003e \u003cp\u003eMethods for Understanding and Explaining Complex Causal AI Models 171\u003c\/p\u003e \u003cp\u003eThe Importance of the SHAP Explainability Method 172\u003c\/p\u003e \u003cp\u003eDetecting Bias and Ensuring Responsible AI 175\u003c\/p\u003e \u003cp\u003eBias in Causal AI Systems 176\u003c\/p\u003e \u003cp\u003eResponsible AI: Trust and Fairness 178\u003c\/p\u003e \u003cp\u003eHow Causal AI Addresses Bias Detection 180\u003c\/p\u003e \u003cp\u003eTools for Assessing Fairness and Bias 182\u003c\/p\u003e \u003cp\u003eThe Human Factor in Bias Detection and Responsible AI 183\u003c\/p\u003e \u003cp\u003eSummary 184\u003c\/p\u003e \u003cp\u003eNote 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Tools, Practices, and Techniques to Enable Causal AI 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Causal AI Pipeline 187\u003c\/p\u003e \u003cp\u003eDefine Business Objectives 190\u003c\/p\u003e \u003cp\u003eModel Development 193\u003c\/p\u003e \u003cp\u003eData Identification and Collection 195\u003c\/p\u003e \u003cp\u003eData Privacy, Governance, and Security 197\u003c\/p\u003e \u003cp\u003eSynthetic Data 198\u003c\/p\u003e \u003cp\u003eModel Validation 199\u003c\/p\u003e \u003cp\u003eDeployment\/Production 201\u003c\/p\u003e \u003cp\u003eMonitor and Evaluate 203\u003c\/p\u003e \u003cp\u003eUpdate and Iterate 205\u003c\/p\u003e \u003cp\u003eContinuous Learning 208\u003c\/p\u003e \u003cp\u003eThe Importance of Synthetic Data 210\u003c\/p\u003e \u003cp\u003eWhy Create Synthetic Data? 210\u003c\/p\u003e \u003cp\u003eOvercoming Data Limitations 211\u003c\/p\u003e \u003cp\u003eEnhancing Data Privacy and Security 211\u003c\/p\u003e \u003cp\u003eModel Validation and Testing 211\u003c\/p\u003e \u003cp\u003eExpanding the Range of Possible Scenarios 212\u003c\/p\u003e \u003cp\u003eReducing the Cost of Data Collection 212\u003c\/p\u003e \u003cp\u003eImproving Data Imbalance 213\u003c\/p\u003e \u003cp\u003eEncouraging Collaboration and Openness 213\u003c\/p\u003e \u003cp\u003eStreamlining Data Preprocessing 213\u003c\/p\u003e \u003cp\u003eSupporting Counterfactual Analysis 213\u003c\/p\u003e \u003cp\u003eFostering Innovation and Experimentation 214\u003c\/p\u003e \u003cp\u003eCreating Synthetic Data 214\u003c\/p\u003e \u003cp\u003eGenerative Models 214\u003c\/p\u003e \u003cp\u003eAgent-Based Modeling 215\u003c\/p\u003e \u003cp\u003eData Augmentation 215\u003c\/p\u003e \u003cp\u003eData Synthesis Tools and Platforms 215\u003c\/p\u003e \u003cp\u003eConditional Synthetic Data Generation 216\u003c\/p\u003e \u003cp\u003eSynthetic Data from Text 216\u003c\/p\u003e \u003cp\u003eThe Limitations of Synthetic Data 217\u003c\/p\u003e \u003cp\u003eCurrent State of Tools and Software in Causal AI 218\u003c\/p\u003e \u003cp\u003eThe Role of Open Source in Causal AI 218\u003c\/p\u003e \u003cp\u003eCommercial Causal AI Software 221\u003c\/p\u003e \u003cp\u003eCausaLens 221\u003c\/p\u003e \u003cp\u003eGeminos Software 223\u003c\/p\u003e \u003cp\u003eSummary 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Causal AI in Action 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnterprise Marketing in a Business- to- Consumer Scenario 226\u003c\/p\u003e \u003cp\u003eDDCo Marketing Causal Model: Annual Pricing Review and Update Cycle 228\u003c\/p\u003e \u003cp\u003eIncorporating Internal and External Factors in the Model and DAG 230\u003c\/p\u003e \u003cp\u003eEasily Enabling Iterating 231\u003c\/p\u003e \u003cp\u003eEnd-User-Driven Exploration 232\u003c\/p\u003e \u003cp\u003eBench Testing 234\u003c\/p\u003e \u003cp\u003eDDCo Marketing Causal Model: Semiannual Product Planning Cycle 236\u003c\/p\u003e \u003cp\u003eAlways Consider Model Reuse 237\u003c\/p\u003e \u003cp\u003eGive and Take in Building a New Model 239\u003c\/p\u003e \u003cp\u003eTypical Model and Process Operation: Iterating 239\u003c\/p\u003e \u003cp\u003eKeeping the Process\/Model Scope Manageable and Understandable 240\u003c\/p\u003e \u003cp\u003eMoving from Strategy to Building and Implementing Causal AI Solutions 241\u003c\/p\u003e \u003cp\u003eAgriculture: Enhancing Crop Yield 242\u003c\/p\u003e \u003cp\u003eKey Causal Variables 244\u003c\/p\u003e \u003cp\u003eCreating the DAG 246\u003c\/p\u003e \u003cp\u003eMoving from the DAG to Implementing the Causal AI Model 247\u003c\/p\u003e \u003cp\u003eCommercial Real Estate: Valuing Warehouse Space 250\u003c\/p\u003e \u003cp\u003eKey Causal Variables 251\u003c\/p\u003e \u003cp\u003eImplementing the Causal AI Model 253\u003c\/p\u003e \u003cp\u003eVideo Streaming: Enhancing Content Recommendations 254\u003c\/p\u003e \u003cp\u003eKey Causal Variables 255\u003c\/p\u003e \u003cp\u003eImplementing the Causal AI Model 256\u003c\/p\u003e \u003cp\u003eHealthcare: Reducing Infant Mortality 258\u003c\/p\u003e \u003cp\u003eKey Causal Variables 259\u003c\/p\u003e \u003cp\u003eImplementing the Causal AI Model 261\u003c\/p\u003e \u003cp\u003eRetail: Providing Executives Actionable Information 263\u003c\/p\u003e \u003cp\u003eKey Causal Variables 264\u003c\/p\u003e \u003cp\u003eImplementing the Causal Model 265\u003c\/p\u003e \u003cp\u003eSummary 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 The Future of Causal AI 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhere We Stand Today 271\u003c\/p\u003e \u003cp\u003eFoundations of Causal AI 273\u003c\/p\u003e \u003cp\u003eThe Causal AI Journey 274\u003c\/p\u003e \u003cp\u003eCausal AI Today 274\u003c\/p\u003e \u003cp\u003eWhat’s Next for Causal AI 276\u003c\/p\u003e \u003cp\u003eIntegrating Causal AI and Traditional AI 278\u003c\/p\u003e \u003cp\u003eThe Imperative for Managing Data 279\u003c\/p\u003e \u003cp\u003eEnsembles of Data 279\u003c\/p\u003e \u003cp\u003eGenerative AI Is Emerging as a Game Changer for Causal AI 281\u003c\/p\u003e \u003cp\u003eThe Future of Causal Discovery 282\u003c\/p\u003e \u003cp\u003eThe Emergence of Causal AI Reinforcement Learning Will Accelerate Model Training 284\u003c\/p\u003e \u003cp\u003eCausal AI as a Common Language Between Business Leaders and Data Scientists 284\u003c\/p\u003e \u003cp\u003eThe Emergence of Probabilistic Programming Languages 286\u003c\/p\u003e \u003cp\u003eThe Predictable Model Evolution Cycle 286\u003c\/p\u003e \u003cp\u003eThe Emergence of the Digital Twin 287\u003c\/p\u003e \u003cp\u003eImproving the Ability to Understand Ground Truth 289\u003c\/p\u003e \u003cp\u003eThe Development of More Sophisticated DAGs 289\u003c\/p\u003e \u003cp\u003eVisualizing Complex Relationships in the DAGs 290\u003c\/p\u003e \u003cp\u003eThe Merging of Causal and Traditional AI Models 291\u003c\/p\u003e \u003cp\u003eThe Future of Explainability 291\u003c\/p\u003e \u003cp\u003eThe Evolution of Responsible AI 292\u003c\/p\u003e \u003cp\u003eAdvances in Data Security and Privacy 293\u003c\/p\u003e \u003cp\u003eIntegration Will Be Between Models and Business Applications 294\u003c\/p\u003e \u003cp\u003eSummary 295\u003c\/p\u003e \u003cp\u003eGlossary 299\u003c\/p\u003e \u003cp\u003eAppendix 313\u003c\/p\u003e \u003cp\u003eSelected Resources 329\u003c\/p\u003e \u003cp\u003eAcknowledgments 331\u003c\/p\u003e \u003cp\u003eAbout the authors 335\u003c\/p\u003e \u003cp\u003eAbout the contributor 339\u003c\/p\u003e \u003cp\u003eIndex 341\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJUDITH S. HURWITZ\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJOHN K. THOMPSON\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eExplore the next major revolution in data science and artificial intelligence: causal AI\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eCausal Artificial Intelligence: The Next Step in Effective Business AI\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eYou’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. \u003c\/p\u003e\u003cp\u003ePerfect for data scientists, subject matter experts in a variety of fields, as well as managers, executives, and other business leaders, \u003ci\u003eCausal Artificial Intelligence\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988893843685,"sku":"NP9781394184132","price":35.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394184132.jpg?v=1761781952","url":"https:\/\/k12savings.com\/es\/products\/causal-artificial-intelligence-isbn-9781394184132","provider":"K12savings","version":"1.0","type":"link"}