Inference And Intervention
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Inference And Intervention
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Author : Michael D. Ryall
language : en
Publisher:
Release Date : 2014
Inference And Intervention written by Michael D. Ryall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Business & Economics categories.
Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which business elements to control or change - as the authors put it, a managerial intervention - must precede any decision on how to control or change them, and understanding causality is crucial to making effective interventions. The authors cover the full spectrum of causal modeling techniques useful for the managerial role, whether for intervention, situational assessment, strategic decision-making, or forecasting. From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions in a managerial setting.
Inference And Intervention
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Author : Michael D. Ryall
language : en
Publisher: Routledge
Release Date : 2013-08-22
Inference And Intervention written by Michael D. Ryall and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-22 with Business & Economics categories.
Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications. In a world of resource scarcity, a decision about which business elements to control or change – as the authors put it, a managerial intervention – must precede any decision on how to control or change them, and understanding causality is crucial to making effective interventions. The authors cover the full spectrum of causal modeling techniques useful for the managerial role, whether for intervention, situational assessment, strategic decision-making, or forecasting. From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions in a managerial setting.
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.
Emerging Issues In Causal Inference For Intervention Trials
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Author : Qi Long
language : en
Publisher:
Release Date : 2005
Emerging Issues In Causal Inference For Intervention Trials written by Qi Long and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
The Effects Of An Inference Instruction Intervention On The Inference Generation And Reading Comprehension Of Struggling Readers In Grades 6 And 7
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Author : Colby S. Hall
language : en
Publisher:
Release Date : 2016
The Effects Of An Inference Instruction Intervention On The Inference Generation And Reading Comprehension Of Struggling Readers In Grades 6 And 7 written by Colby S. Hall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
There is ample evidence that inference generation skill directly contributes to reading comprehension, as well as evidence that struggling readers make fewer inferences than proficient readers. This experimental study examined the effectiveness of a small-group inference instruction intervention on the inference generation and reading comprehension of struggling readers in Grades 6 and 7. The sample comprised 78 students randomly assigned to a small-group inference instruction intervention condition (n = 39) or a business-as-usual comparison condition in which students received computer-delivered English language arts instruction via individualized learning software (n = 39). In the intervention condition, small groups of 3 to 6 students participated in 24, 40-minute sessions. Instruction focused on both text-connecting inferences (e.g., pronoun reference, inferring word meaning from context) and gap-filling inferences (i.e., inferences that require students to integrate their knowledge about the world with information in text). Treatment effects were estimated using multiple regression analyses. Results indicate that membership in the Making Inferences treatment condition statistically significantly predicted higher outcome score for the standardized measure of general reading comprehension skill, the GMRT Reading Comprehension subtest (d = 0.60), but not for any of the three measures of inference skill. Phonemic decoding at pretest was a statistically significant moderator of intervention effects on the GMRT-RC, with treatment effects increasing as students’ levels of phonemic decoding skill increased. The same pattern of effects was evident for the depth of vocabulary knowledge moderator variables, although interaction terms were not statistically significant, p
Causal Inference From Interventional Data
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Author : Alain Hauser
language : en
Publisher:
Release Date : 2013
Causal Inference From Interventional Data written by Alain Hauser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
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.
Intervention Experiments Randomization And Inference
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Author : O. Kempthorne
language : en
Publisher:
Release Date : 1988
Intervention Experiments Randomization And Inference written by O. Kempthorne and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with categories.
Healthcare System Access
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Author : Nicoleta Serban
language : en
Publisher: John Wiley & Sons
Release Date : 2019-12-24
Healthcare System Access written by Nicoleta Serban and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-24 with Medical categories.
A guide to a holistic approach to healthcare measurement aimed at improving access and outcomes Healthcare System Access is an important resource that bridges two areas of research—access modeling and healthcare system engineering. The book’s mathematical modeling approach highlights fundamental approaches on measurement of and inference on healthcare access. This mathematical modeling facilitates translating data into knowledge in order to make data-driven estimates and projections about parameters, patterns, and trends in the system. The complementary engineering approach uses estimates and projections about the system to better inform efforts to design systems that will yield better outcomes. The author—a noted expert on the topic—offers an in-depth exploration of the concepts of systematic disparities, reviews measures for systematic disparities, and presents a statistical framework for making inference on disparities with application to disparities in access. The book also includes information health outcomes in the context of prevention and chronic disease management. In addition, this text: Integrates data and knowledge from various fields to provide a framework for decision making in transforming access to healthcare Provides in-depth material including illustrations of how to use state-of-art methodology, large data sources, and research from various fields Includes end-of-chapter case studies for applying concepts to real-world conditions Written for health systems engineers, Healthcare System Access: Measurement, Inference, and Intervention puts the focus on approaches to measure healthcare access and addresses important enablers of such change in healthcare towards improving access and outcomes.
Causal Inference In Python
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Author : Matheus Facure
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-14
Causal Inference In Python written by Matheus Facure and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-14 with categories.
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution