Causal Learning
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Causal Learning
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Author :
language : en
Publisher: Academic Press
Release Date : 1996-09-26
Causal Learning written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-09-26 with Psychology categories.
The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditions to complex learning and problem solving. This guest-edited special volume is devoted to current research and discussion on associative versus cognitive accounts of learning. Written by major investigators in the field, topics include all aspects of causal learning in an open forum in which different approaches are brought together. Up-to-date review of the literature Discusses recent controversies Presents major advances in understanding causal learning Synthesizes contrasting approaches Includes important empirical contributions Written by leading researchers in the field
Causal Learning
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Author : Alison Gopnik
language : en
Publisher: Oxford University Press
Release Date : 2007-03-22
Causal Learning written by Alison Gopnik and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-22 with Family & Relationships categories.
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Elements Of Causal Inference
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Author : Jonas Peters
language : en
Publisher: MIT Press
Release Date : 2017-11-29
Elements Of Causal Inference written by Jonas Peters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-29 with Computers categories.
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.
Causal Learning
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Author : Keith James Holyoak
language : en
Publisher:
Release Date : 1996
Causal Learning written by Keith James Holyoak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Learning in animals categories.
An Introduction To Causal Inference
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Author : Judea Pearl
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2015
An Introduction To Causal Inference written by Judea Pearl and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Causation categories.
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.
Targeted Learning
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Author : Mark J. van der Laan
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-17
Targeted Learning written by Mark J. van der Laan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-17 with Mathematics categories.
The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
The Oxford Handbook Of Causal Reasoning
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Author : Michael Waldmann
language : en
Publisher: Oxford University Press
Release Date : 2017-03-30
The Oxford Handbook Of Causal Reasoning written by Michael Waldmann and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-30 with Psychology categories.
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.
Targeted Learning In Data Science
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Author : Mark J. van der Laan
language : en
Publisher: Springer
Release Date : 2018-03-28
Targeted Learning In Data Science written by Mark J. van der Laan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-28 with Mathematics categories.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
Time And Causality
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Author : Marc J. Buehner
language : en
Publisher: Frontiers E-books
Release Date : 2014-08-06
Time And Causality written by Marc J. Buehner and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-06 with Causation categories.
The problem of how humans and other intelligent systems construct causal representations from non-causal perceptual evidence has occupied scholars in cognitive science for many decades. Most contemporary approaches agree with David Hume that patterns of covariation between two events of interest are the critical input to the causal induction engine, irrespective of whether this induction is believed to be grounded in the formation of associations (Shanks & Dickinson, 1987), rule-based evaluation (White, 2004), appraisal of causal powers (Cheng, 1997), or construction of Bayesian Causal Networks (Pearl, 2000). Recent research, however, has repeatedly demonstrated that an exclusive focus on covariation while neglecting contiguity (another of Hume’s cues) results in ecologically invalid models of causal inference. Temporal spacing, order, variability, predictability, and patterning all have profound influence on the type of causal representation that is constructed. The influence of time upon causal representations could be seen as a bottom-up constraint (though current bottom-up models cannot account for the full spectrum of effects). However, causal representations in turn also constrain the perception of time: Put simply, two causally related events appear closer in subjective time than two (equidistant) unrelated events. This reversal of Hume’s conjecture, referred to as Causal Binding (Buehner & Humphreys, 2009) is a top-down constraint, and suggests that our representations of time and causality are mutually influencing one another. At present, the theoretical implications of this phenomenon are not yet fully understood. Some accounts link it exclusively to human motor planning (appealing to mechanisms of cross-modal temporal adaptation, or forward learning models of motor control). However, recent demonstrations of causal binding in the absence of human action, and analogous binding effects in the visual spatial domain, challenge such accounts in favour of Bayesian Evidence Integration. This Research Topic reviews and further explores the nature of the mutual influence between time and causality, how causal knowledge is constructed in the context of time, and how it in turn shapes and alters our perception of time. We draw together literatures from the perception and cognitive science, as well as experimental and theoretical papers. Contributions investigate the neural bases of binding and causal learning/perception, methodological advances, and functional implications of causal learning and perception in real time.
Artificial Intelligence And Causal Inference
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Author : Momiao Xiong
language : en
Publisher: CRC Press
Release Date : 2022-02-03
Artificial Intelligence And Causal Inference written by Momiao Xiong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-03 with Business & Economics categories.
Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features: Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training. Deep learning for nonlinear mediation and instrumental variable causal analysis. Construction of causal networks is formulated as a continuous optimization problem. Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks. Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes. AI-based methods for estimation of individualized treatment effect in the presence of network interference.