Causal Inference For Data Scientists

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Causal Inference For Data Scientists

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Author : Adam Kelleher
language : en
Publisher: Addison-Wesley
Release Date : 2019-10-16

Causal Inference For Data Scientists written by Adam Kelleher and has been published by Addison-Wesley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-16 with categories.

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.

Causal Inference For Data Science

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Author : Alex Ruiz de Villa
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-21

Causal Inference For Data Science written by Alex Ruiz de Villa and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-21 with Computers categories.

Causal Inference for Data Science introduces data-centric techniques and methodologies you can use to estimate causal effects. The numerous insightful examples show you how to put causal inference into practice in the real world. The practical techniques presented in this unique book are accessible to anyone with intermediate data science skills and require no advanced statistics!

Causation In Population Health Informatics And Data Science

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Author : Olaf Dammann
language : en
Publisher: Springer
Release Date : 2018-10-29

Causation In Population Health Informatics And Data Science written by Olaf Dammann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-29 with Medical categories.

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

Winning With Data Science

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Author : Howard Steven Friedman
language : en
Publisher: Columbia University Press
Release Date : 2024-01-30

Winning With Data Science written by Howard Steven Friedman and has been published by Columbia University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.

Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject.

Cause And Effect Business Analytics And Data Science

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Author : Dominique Haughton
language : en
Publisher: CRC Press
Release Date : 2025-07-15

Cause And Effect Business Analytics And Data Science written by Dominique Haughton and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-15 with Business & Economics categories.

Among the most important questions that businesses ask are some very simple ones: If I decide to do something, will it work? And if so, how large are the effects? To answer these predictive questions, and later base decisions on them, we need to establish causal relationships. Establishing and measuring causality can be difficult. This book explains the most useful techniques for discerning causality and illustrates the principles with numerous examples from business. It discusses randomized experiments (aka A/B testing) and techniques such as propensity score matching, synthetic controls, double differences, and instrumental variables. There is a chapter on the powerful AI approach of Directed Acyclic Graphs (aka Bayesian Networks), another on structural equation models, and one on time-series techniques, including Granger causality. At the heart of the book are four chapters on uplift modeling, where the goal is to help firms determine how best to deploy their resources for marketing or other interventions. We start by modeling uplift, discuss the test-and-learn process, and provide an overview of the prescriptive analytics of uplift. The book is written in an accessible style and will be of interest to data analysts and strategists in business, to students and instructors of business and analytics who have a solid foundation in statistics, and to data scientists who recognize the need to take seriously the need for causality as an essential input into effective decision-making.

Data Science For Public Policy

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Author : Jeffrey C. Chen
language : en
Publisher: Springer Nature
Release Date : 2021-09-01

Data Science For Public Policy written by Jeffrey C. Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Mathematics categories.

This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

Causal Ai

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Author : Robert Osazuwa Ness
language : en
Publisher: Simon and Schuster
Release Date : 2025-03-18

Causal Ai written by Robert Osazuwa Ness and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Computers categories.

Causal AI is a practical introduction to building AI models that can reason about causality. Robert Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.

Managing Data Science

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Author : Kirill Dubovikov
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-12

Managing Data Science written by Kirill Dubovikov and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Computers categories.

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Data Science In Context

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Author : Alfred Z. Spector
language : en
Publisher: Cambridge University Press
Release Date : 2022-10-20

Data Science In Context written by Alfred Z. Spector and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Business & Economics categories.

Four leading experts convey the promise of data science and examine challenges in achieving its benefits and mitigating some harms.