Download or read book Reasoning Web Causality Explanations and Declarative Knowledge written by Leopoldo Bertossi and published by Springer Nature. This book was released on 2023-04-27 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers. The broad theme of this year's summer school was “Reasoning in Probabilistic Models and Machine Learning” and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures were presented during the school: Logic-Based Explainability in Machine Learning; Causal Explanations and Fairness in Data; Statistical Relational Extensions of Answer Set Programming; Vadalog: Its Extensions and Business Applications; Cross-Modal Knowledge Discovery, Inference, and Challenges; Reasoning with Tractable Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.

Download or read book Handbook of Matching and Weighting Adjustments for Causal Inference written by José R. Zubizarreta and published by CRC Press. This book was released on 2023-04-11 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

Download or read book Rethinking Value Added Models in Education written by Audrey Amrein-Beardsley and published by Routledge. This book was released on 2014-04-24 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since passage of the of No Child Left Behind Act in 2001, academic researchers, econometricians, and statisticians have been exploring various analytical methods of documenting students‘ academic progress over time. Known as value-added models (VAMs), these methods are meant to measure the value a teacher or school adds to student learning from one year to the next. To date, however, there is very little evidence to support the trustworthiness of these models. What is becoming increasingly evident, yet often ignored mainly by policymakers, is that VAMs are 1) unreliable, 2) invalid, 3) nontransparent, 4) unfair, 5) fraught with measurement errors and 6) being inappropriately used to make consequential decisions regarding such things as teacher pay, retention, and termination. Unfortunately, their unintended consequences are not fully recognized at this point either. Given such, the timeliness of this well-researched and thoughtful book cannot be overstated. This book sheds important light on the debate surrounding VAMs and thereby offers states and practitioners a highly important resource from which they can move forward in more research-based ways.

Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Download or read book Machine Learning for Causal Inference written by Sheng Li and published by Springer Nature. This book was released on 2023-11-25 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.

Download or read book Designing Social Inquiry written by Gary King and published by Princeton University Press. This book was released on 1994-05-22 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?

Download or read book Reclaiming the Public written by Avihay Dorfman and published by Cambridge University Press. This book was released on 2024-02-29 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develops a political theory of the public and of political authority and elaborates the theory's legal and institutional implications.

Download or read book XxAI Beyond Explainable AI written by Andreas Holzinger and published by Springer Nature. This book was released on 2022 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.

Download or read book Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and published by "O'Reilly Media, Inc.". This book was released on 2023-01-03 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention

Download or read book Experimental and Quasi experimental Designs for Generalized Causal Inference written by William R. Shadish and published by Cengage Learning. This book was released on 2002 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions.

Download or read book Causation Prediction and Search written by Peter Spirtes and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.

Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Handbook of Consumer Psychology written by Curtis P. Haugtvedt and published by Routledge. This book was released on 2018-12-07 with total page 1257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook contains a unique collection of chapters written by the world's leading researchers in the dynamic field of consumer psychology. Although these researchers are housed in different academic departments (ie. marketing, psychology, advertising, communications) all have the common goal of attaining a better scientific understanding of cognitive, affective, and behavioral responses to products and services, the marketing of these products and services, and societal and ethical concerns associated with marketing processes. Consumer psychology is a discipline at the interface of marketing, advertising and psychology. The research in this area focuses on fundamental psychological processes as well as on issues associated with the use of theoretical principles in applied contexts. The Handbook presents state-of-the-art research as well as providing a place for authors to put forward suggestions for future research and practice. The Handbook is most appropriate for graduate level courses in marketing, psychology, communications, consumer behavior and advertising.

Download or read book Intelligent Systems and Applications written by Kohei Arai and published by Springer Nature. This book was released on with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Download or read book Thrive written by Ravi Bapna and published by MIT Press. This book was released on 2024-10-08 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: How AI can positively impact so many aspects of our daily lives, from health and wellness to work, education, and home life. Artificial intelligence (AI) is a powerful general-purpose technology that is reshaping the modern economy, but misperceptions about AI stand in the way of harnessing it for the betterment of humanity. In Thrive, Ravi Bapna and Anindya Ghose counter the backlash by showcasing how AI is positively influencing the aspects of our daily lives that we care about most: our health and wellness, relationships, education, the workplace, and domestic life. In the process the authors help explain the underlying technology and give people the agency they need to shape the debate around how we should regulate AI to maximize its benefits and minimize its risks. Bringing over two decades of experience with cutting-edge research, consulting, executive coaching, and advising to bear on the subject, Bapna and Ghose demystify the technology of AI itself. They offer a novel “House of AI” framework that encompasses traditional analytics, generative AI, and fair and ethical deployment of AI. Using examples from everyday life, they showcase how the modern AI-powered ecosystem fundamentally improves the emotional, physical, and material well-being of regular people across the globe. Thrive’s mission is to educate the public about AI, shape realistic expectations, and foster informed discussions about a fast-emerging AI-shaped society.

Download or read book The Grammar of Graphics written by Leland Wilkinson and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. It was designed for a distributed computing environment, with special attention given to conserving computer code and system resources. While the tangible result of this work is a Java production graphics library, the text focuses on the deep structures involved in producing quantitative graphics from data. It investigates the rules that underlie pie charts, bar charts, scatterplots, function plots, maps, mosaics, and radar charts. These rules are abstracted from the work of Bertin, Cleveland, Kosslyn, MacEachren, Pinker, Tufte, Tukey, Tobler, and other theorists of quantitative graphics.