Causality - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Causality write by Judea Pearl. This book was released on 2009-09-14. Causality available in PDF, EPUB and Kindle. Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...
Causality and Causal Modelling in the Social Sciences
Causality and Causal Modelling in the Social Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Causality and Causal Modelling in the Social Sciences write by Federica Russo. This book was released on 2008-09-18. Causality and Causal Modelling in the Social Sciences available in PDF, EPUB and Kindle. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science.
Causal Models
Causal Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Causal Models write by Steven Sloman. This book was released on 2005-07-28. Causal Models available in PDF, EPUB and Kindle. Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. In cognitive terms, how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. How does intervening on one thing affect other things? This is not a question merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.
The Mind's Arrows
The Mind's Arrows - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Mind's Arrows write by Clark N. Glymour. This book was released on 2001. The Mind's Arrows available in PDF, EPUB and Kindle. This title provides an introduction to assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. It demonstrates their potential as a powerful tool for guiding experimental inquiry.
Elements of Causal Inference
Elements of Causal Inference - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Elements of Causal Inference write by Jonas Peters. This book was released on 2017-11-29. Elements of Causal Inference available in PDF, EPUB and Kindle. A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
