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Titlebook: Causal Analytics for Applied Risk Analysis; Louis Anthony Cox Jr.,Douglas A. Popken,Richard X. Book 2018 Springer International Publishing

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发表于 2025-3-21 18:09:52 | 显示全部楼层 |阅读模式
书目名称Causal Analytics for Applied Risk Analysis
编辑Louis Anthony Cox Jr.,Douglas A. Popken,Richard X.
视频video
概述Highlights causal analytics methods that answer how changing decision variables can change probabilities of various outcomes.Presents models, algorithms, principles, and software for deriving causal m
丛书名称International Series in Operations Research & Management Science
图书封面Titlebook: Causal Analytics for Applied Risk Analysis;  Louis Anthony Cox Jr.,Douglas A. Popken,Richard X. Book 2018 Springer International Publishing
描述Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit
出版日期Book 2018
关键词Decision Making; Risk Analysis; Risk Management; Risk Models; Causal Analytics; Risk Analytics; Descriptiv
版次1
doihttps://doi.org/10.1007/978-3-319-78242-3
isbn_softcover978-3-030-08653-4
isbn_ebook978-3-319-78242-3Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Descriptive Analytics for Public Health: Socioeconomic and Air Pollution Correlates of Adult Asthma,ses basic descriptive information: how big is a risk now, how is it changing over time or with age, how does it differ for people or situations with different characteristics, on what factors does it depend, with what other risks or characteristics does it cluster? Such questions arise not only for
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How Large Are Human Health Risks Caused by Antibiotics Used in Food Animals?umption of food contaminated with disease-causing bacteria and antibiotic-resistant “superbugs” sparks strong political passions, dramatic media headlines, and heated science-policy debates (Chang et al. 2014). A widespread concern is that use of animal antibiotics on farms creates selection pressur
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Attributive Causal Modeling: Quantifying Human Health Risks Caused by Toxoplasmosis from Open System challenge in this one is to estimate human health risks from a pathogen in swine using a combination of plausible conservative estimates of relevant risk factors and probabilistic simulation. However, our focus now shifts to predicting how risks would . if some fraction of swine were shifted from t
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How Well Can High-Throughput Screening Tests Results Predict Whether Chemicals Cause Cancer in Mice l intelligence, machine-learning and bioinformatics has been to predict . biological responses to realistic exposures, with demonstrably useful accuracy and confidence, from . and chemical structure data. The common goal of many applied research efforts has been to devise and validate algorithms tha
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Mechanistic Causality: Biological Mechanisms of Dose-Response Thresholds for Inflammation-Mediated Dvalidate than other forms of causal analysis, including predictive and attributive causal modeling. Substantial applied and computational mathematical research, modeling, and algorithm development is sometimes needed to describe with useful accuracy how a system evolves over time. On the other hand,
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Evaluation Analytics for Public Health: Has Reducing Air Pollution Reduced Death Rates in the Unitedy analysts and epidemiologists, this includes drawing inferences about whether historical changes in exposures have actually caused the consequences predicted for, or attributed to, them. The example of the Dublin coal-burning ban introduced in Chap. . suggests that accurate evaluation of the effect
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