Causal Artificial Intelligence

Preface

In my view, causal AI is the next stage in the evolution of software because it is focused on being able to understand the causes and effects of events. As we discuss in this book, what has caused a marketing campaign to achieve the revenue objectives? Is the problem the campaign itself, or are there underlying issues that are impacting results? Is the cause of the disappointing marketing campaign because of a sudden competitive threat? Is there a problem with the company's reputation? What would the impact on revenue if the product price was reduced by 10 percent? Would a different type of marketing campaign result in better results? The underlying casual technology needed to address these problems is complex, and the approach is instrumental for business leaders to understand the potential impact. Therefore, unlike some earlier evolutions of AI, the value of a causal AI approach can have a direct and profound effect on business outcomes.

A plethora of books and articles already address causal inference—a field that must recognize Judea Pearl as a pioneer and visionary in causality. So, why write yet another book on the topic? The reason is straightforward—this book is written for technology-focused leaders who are not developers but are responsible for bringing new technology into their companies to gain a competitive edge. In writing this book, I have spent countless hours speaking with leaders in the field and reading many articles and books. The goal of this book ...