This book provides a post-positivist theory of deterministic and probabilistic causality that supports both quantitative and qualitative explanations. Features of particular interest include the ability to provide true explanations in contexts where our knowledge is incomplete, a systematic interpretation of causal modeling techniques in the social sciences, and a direct realist view of causal relations that is compatible with a liberal empiricism. The book should be of wide interest to both philosophers and scientists. Originally published in 1989. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

For over two decades Wesley Salmon has helped to shape the course of debate in philosophy of science. He is a major contributor to the philosophical discussion of problems associated with causality and the author of two influential books on scientific explanation. This long-awaited volume collects twenty- six of Salmon's essays, including seven that have never before been published and others difficult to find. Part I comprises five introductory essays that presuppose no formal training in philosophy of science and form a background for subsequent essays. Parts II and III contain Salmon's seminal work on scientific explanation and causality. Part IV offers survey articles that feature advanced material but remain accessible to those outside philosophy of science. Essays in Part V address specific issues in particular scientific disciplines, namely, archaeology and anthropology, astrophysics and cosmology, and physics. Clear, compelling, and essential, this volume offers a superb introduction to philosophy of science for nonspecialists and belongs on the bookshelf of all who carry out work in this exciting field. Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes a section of accessible introductory pieces, as well as more advanced and technical pieces, and will make essential work in the philosophy of science readily available to both scholars and students.

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

Chance fills every day of our lives and affects every decision we make. Yet, for something woven so closely into the fabric of our being, we are not very good at thinking about uncertainty and risk. In this lively and engaging book, Andrew C. A. Elliott asks why this is so. He picks at the threads and, in showing how our world is built on probability rather than certainty, he identifies five obstacles to thinking about uncertainty that confuse us time after time. Elliott takes us into the casino, but this is not an invitation to gamble. He looks at financial markets, but this is not a guide to investment. There's discussion of health, but this is not a medical book. He touches on genetics and evolution, and music-making, and writing, because chance is at work there too. Entering many different fields, What are the Chances of That? is always following the trail of chance and randomness. One purpose of the book is to go cross-country, to show that there are connected ways of thinking that disrespect boundaries and cut across the domains of finance, and gambling, and genetics, and public health, and creativity. Through it, one visits the vantage points that give a broad view of the landscape and sees how these different areas of life and knowledge are connected - through chance. What are the Chances of That? discusses chance and the importance of understanding how it affects our lives. It goes beyond a mathematical approach to the subject, showing how our thinking about chance and uncertainty has been shaped by history and culture, and only relatively recently by the mathematical theory of probability. In considering how we think about uncertainty, Elliott proposes five “dualities” that encapsulate many of the ambiguities that arise.

The nature of scientific explanation has been an important topic in philosophy of science for many years. This book highlights some of the conceptual problems that still need to be solved and points out a number of fresh philosophical ideas to explore.

Leading scholars discuss the development and application of theories of causation and explanation, offering a state-of-the-art view of current work on these two topics.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.