PDF Cause and Effect Download
- Author: Sven Ehmann
- Publisher: Die Gestalten Verlag-DGV
- ISBN: 9783899554434
- Category : Design
- Languages : en
- Pages : 0
This book reveals the new visual language of sustainability.
eBook downloads, eBook resources & eBook authors
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.
Cause uses sociology's tools to explain how, as humans in general, we are bad at determining cause and effect, particularly when we're trying to understand social problems like poverty, discrimination, or how to respond to climate change and terrorism. Divided into three sections, the book examines how and why humans tell stories; the unseen influences that we overlook when telling these stories; and how a smarter story could greatly enhance how we understand ourselves and each other. Cause offers nothing short of a new way of looking at our world.
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences
The Cold War was the strategic and ideological competition between the United States and the Soviet Union for world supremacy. Through thoughtful narrative supported by fully documented quotes, this title begins with A Brief History of the Cold War and then examines these questions: How Did Stalin¿s Postwar Strategy Lead to the Start of the Cold War? What Effect Did the McCarthy Hearings Have on Cold War Policies? How Did the Cuban Missile Crisis Affect US and Soviet Cold War Strategies? Did the American Military Buildup in the 1980s Help End the Cold War?
Cause and effect is an essential reading comprehension skill for all subject areas. Help students understand cause and effect using Spotlight on Reading: Cause and Effect for grades 5–6. This 48-page book includes a variety of high-interest lessons and activities that make learning fun! The exercises increase in difficulty as the book progresses, so students practice more-advanced skills as they work. With a variety of formats, teachers can provide direct instruction, reinforcement, and independent practice throughout the year. This book is perfect for practice at home and school and includes an answer key. It aligns with state, national, and Canadian provincial standards.
The Cause & Effect in History series examines major historic events by focusing on specific causes and consequences. For instance, in Cause & Effect: The French Revolution, a chapter explores how inequality led to the revolution. And in Cause & Effect: The American Revolution, one chapter delves into this question: "How did assistance from France help the American cause?" Every book in the series includes thoughtful discussion of questions like these - supported by facts, examples, and a mix of fully documented primary and secondary source quotes. Each title also includes an overview of the event so that readers have a broad context for understanding the more detailed discussions of specific causes and their effects. Book jacket.
This book focuses on numerous examples of tasks represented by c-e structure. Cause–effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify behavior of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.