Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation. In virtually every laboratory, research center, or classroom focused on the social or physical sciences today, the concept of causation is a core issue to be questioned, tested, and determined. Even debates in unrelated areas such as biology, law, and philosophy often focus on causality—"What made that happen?" In this book, experts from across disciplines adopt a reader-friendly approach to reconsider this age-old question in a modern light, defining different kinds of causation and examining how causes and consequences are framed and approached in a particular field. Each chapter uses applied examples to illustrate key points in an accessible manner. The contributors to this work supply a coherent critical analysis of the assumptions researchers and practitioners hold about causation, and explain how such thinking about causation can be improved. Collectively, the coverage is broad, providing readers with a fuller picture of research in social contexts. Beyond providing insightful description and thought-provoking questioning of causation in different research areas, the book applies analysis of data in order to point the way to smarter, more efficient practices. Consequently, both practitioners and researchers will benefit from this book.

Head hits cause brain damage - but not always. Should we ban sport to protect athletes? Exposure to electromagnetic fields is strongly associated with cancer development - does that mean exposure causes cancer? Should we encourage old fashioned communication instead of mobile phones to reduce cancer rates? According to popular wisdom, the Mediterranean diet keeps you healthy. Is this belief scientifically sound? Should public health bodies encourage consumption of fresh fruit and vegetables? Severe financial constraints on research and public policy, media pressure, and public anxiety make such questions of immense current concern not just to philosophers but to scientists, governments, public bodies, and the general public. In the last decade there has been an explosion of theorizing about causality in philosophy, and also in the sciences. This literature is both fascinating and important, but it is involved and highly technical. This makes it inaccessible to many who would like to use it, philosophers and scientists alike. This book is an introduction to philosophy of causality - one that is highly accessible: to scientists unacquainted with philosophy, to philosophers unacquainted with science, and to anyone else lost in the labyrinth of philosophical theories of causality. It presents key philosophical accounts, concepts and methods, using examples from the sciences to show how to apply philosophical debates to scientific problems.

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Although causal reasoning is a component of most of our cognitive functions, it has been neglected in cognitive psychology for many decades. The Oxford Handbook of Causal Reasoning offers a state-of-the-art review of the growing field, and its contribution to the world of cognitive science. The Handbook begins with an introduction of competing theories of causal learning and reasoning. In the next section, it presents research about basic cognitive functions involved in causal cognition, such as perception, categorization, argumentation, decision-making, and induction. The following section examines research on domains that embody causal relations, including intuitive physics, legal and moral reasoning, psychopathology, language, social cognition, and the roles of space and time. The final section presents research from neighboring fields that study developmental, phylogenetic, and cultural differences in causal cognition. The chapters, each written by renowned researchers in their field, fill in the gaps of many cognitive psychology textbooks, emphasizing the crucial role of causal structures in our everyday lives. This Handbook is an essential read for students and researchers of the cognitive sciences, including cognitive, developmental, social, comparative, and cross-cultural psychology; philosophy; methodology; statistics; artificial intelligence; and machine learning.

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.

Causality is one of the core concepts in any attempt to make sense of the world, and the explanations people come up with shape their judgments, emotions, intentions and actions. This renders causal cognition a core topic for the social as well as the cognitive sciences. In the past, however, research has been split into diverging paradigms, each pertaining to a distinct (sub)discipline and focusing on a specific domain, thus creating a rather fragmented picture of causal cognition. Furthermore, most of this previous research paid only incidental attention to culture as a possibly constitutive factor, leaving important questions unanswered: Is causality always perceived in the same way? Are causal explanations affected by the concepts to which people refer and/or the language they use? Is causal cognition domain-specific, and if so, how does it differ from agency construal? Is causal reasoning always based on the same cognitive mechanisms, or does the cultural background of people shape how they process respective information - and perhaps even their willingness to search for causal explanations in the first place? By soliciting contributions that address questions like these, this research topic aimed at assessing the extent to which causal cognition may vary across species, cultures, or individuals at various stages of their development, and at integrating different perspectives across a broad range of disciplines. Originating from the work of a research group funded by the Center for Interdisciplinary Research (ZiF) at Bielefeld University, Germany, the scope of this research topic was broadened by inviting additional contributions from researchers with expertise in different fields of causal cognition, agency construal, and/or cultural impacts on cognition. In order to fully exploit the potential of cognitive science, we explicitly encouraged submissions from scholars from all its classic sub-disciplines (i.e., anthropology, artificial intelligence, linguistics, neuroscience, philosophy, psychology) as well as scholars from comparative psychology, cognitive archeology, economics, and any other discipline interested in causal cognition. We welcomed empirical findings as well as theoretical contributions, with an emphasis on those factors that do – or may – constrain, trigger, or shape the way in which humans and other primates think about causal relationships and inform us about both the diversity and the universality of causal cognition.