Causation And Prediction Challenge
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| Author | : Isabelle Guyon |
| Publisher | : |
| Total Pages | : 294 |
| Release | : 2010-11-01 |
| Genre | : Computers |
| ISBN | : 9780971977723 |
This volume gathers the material of the first causality challenge organized by the Causality Workbench Team for the World Congress on Computational Intelligence (WCCI), June 3, 2008 in Hong Kong, including a collection of papers first published in the Journal of Machine Learning Research and a paper summarizing the results of the challenge and contributions of the top ranking entrants. An appendix describes the methods used by participants and a technical report with details on the datasets. The book is complemented by a web site from which the datasets can be downloaded and post-challenge submissions can be made to benchmark new algorithms.
| Author | : Peter Spirtes |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 551 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461227488 |
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.
| Author | : Clark N. Glymour |
| Publisher | : |
| Total Pages | : |
| Release | : 1999 |
| Genre | : Causation |
| ISBN | : 9780262315821 |
| Author | : Nguyen Ngoc Thach |
| Publisher | : Springer Nature |
| Total Pages | : 691 |
| Release | : 2021-07-26 |
| Genre | : Technology & Engineering |
| ISBN | : 303077094X |
This book provides the ultimate goal of economic studies to predict how the economy develops—and what will happen if we implement different policies. To be able to do that, we need to have a good understanding of what causes what in economics. Prediction and causality in economics are the main topics of this book's chapters; they use both more traditional and more innovative techniques—including quantum ideas -- to make predictions about the world economy (international trade, exchange rates), about a country's economy (gross domestic product, stock index, inflation rate), and about individual enterprises, banks, and micro-finance institutions: their future performance (including the risk of bankruptcy), their stock prices, and their liquidity. Several papers study how COVID-19 has influenced the world economy. This book helps practitioners and researchers to learn more about prediction and causality in economics -- and to further develop this important research direction.
| Author | : Jonas Peters |
| Publisher | : MIT Press |
| Total Pages | : 289 |
| Release | : 2017-11-29 |
| Genre | : Computers |
| ISBN | : 0262037319 |
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.
| Author | : |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2002 |
| Genre | : |
| ISBN | : 9780262194402 |
| Author | : Judea Pearl |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 0 |
| Release | : 2015 |
| Genre | : Causation |
| ISBN | : 9781507894293 |
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
| Author | : Michael R. Berthold |
| Publisher | : Springer |
| Total Pages | : 588 |
| Release | : 2020-04-02 |
| Genre | : Computers |
| ISBN | : 9783030445836 |
This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
| Author | : Florin Popescu |
| Publisher | : |
| Total Pages | : 152 |
| Release | : 2013-06 |
| Genre | : Computers |
| ISBN | : 9780971977754 |
This volume in the Challenges in Machine Learning series gathers papers from the Mini Symposium on Causality in Time Series, which was part of the Neural Information Processing Systems (NIPS) confernce in 2009 in Vancouver, Canada. These papers present state-of-the-art research in time-series causality to the machine learning community, unifying methodological interests in the various communities that require such inference.
| Author | : Clark N. Glymour |
| Publisher | : |
| Total Pages | : 576 |
| Release | : 1999 |
| Genre | : Business & Economics |
| ISBN | : |
In science, business, and policymaking -- anywhere data are used in prediction -- two sorts of problems requiring very different methods of analysis often arise. The first, problems of recognition and classification, concerns learning how to use some features of a system to accurately predict other features of that system. The second, problems of causal discovery, concerns learning how to predict those changes to some features of a system that will result if an intervention changes other features. This book is about the second -- much more difficult -- type of problem. Typical problems of causal discovery are: How will a change in commission rates affect the total sales of a company? How will a reduction in cigarette smoking among older smokers affect their life expectancy? How will a change in the formula a college uses to award scholarships affect its dropout rate? These sorts of changes are interventions that directly alter some features of the system and perhaps -- and this is the question -- indirectly alter others. The contributors discuss recent research and applications using Bayes nets or directed graphic representations, including representations of feedback or recursive systems. The book contains a thorough discussion of foundational issues, algorithms, proof techniques, and applications to economics, physics, biology, educational research, and other areas.
