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Author :
Publisher :
ISBN 13 :
Total Pages : 332 pages
Book Rating : 4.:/5 (129 download)
Download or read book Methodological Obstacles in Causal Inference written by and published by . This book was released on 2022 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Byung-Jae Lee
Publisher :
ISBN 13 :
Total Pages : 326 pages
Book Rating : 4.:/5 (891 download)
Download or read book Methodological Problems in Causal Inference, with Reference to Transitional Justice written by Byung-Jae Lee and published by . This book was released on 2014 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses methodological problems in causal inference in the presence of time-varying confounding, and provides methodological tools to handle the problems within the potential outcomes framework of causal inference. The time-varying confounding is common in longitudinal observational studies, in which the covariates and treatments are interacting and changing over time in response to the intermediate outcomes and changing circumstances. The existing approaches in causal inference are mostly focused on static single-shot decision-making settings, and have limitations in estimating the effects of long-term treatments on the chronic problems. In this dissertation, I attempt to conceptualize the causal inference in this situation as a sequential decision problem, using the conceptual tools developed in decision theory, dynamic treatment regimes, and machine learning. I also provide methodological tools useful for this situation, especially when the treatments are multi-level and changing over time, using inverse probability weights and $g$-estimation. Substantively, this dissertation examines transitional justice's effects on human rights and democracy in emerging democracies. Using transitional justice as an example to illustrate the proposed methods, I conceptualize the adoption of transitional justice by a new government as a sequential decision-making process, and empirically examine the comparative effectiveness of transitional justice measures --- independently or in combination with others --- on human rights and democracy.
Author : Jonas Peters
Publisher : MIT Press
ISBN 13 : 0262037319
Total Pages : 289 pages
Book Rating : 4.2/5 (62 download)
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Mark J. van der Laan
Publisher : Springer
ISBN 13 : 3319653040
Total Pages : 655 pages
Book Rating : 4.3/5 (196 download)
Download or read book Targeted Learning in Data Science written by Mark J. van der Laan and published by Springer. This book was released on 2018-03-28 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Richard J. Murnane
Publisher : Oxford University Press
ISBN 13 : 0199780315
Total Pages : 414 pages
Book Rating : 4.1/5 (997 download)
Download or read book Methods Matter written by Richard J. Murnane and published by Oxford University Press. This book was released on 2010-09-15 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.
Author : Barbara Schneider
Publisher :
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4.X/5 (3 download)
Download or read book Estimating Causal Effects written by Barbara Schneider and published by . This book was released on 2007 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the value of quasi-experimental techniques that can be used to approximate randomized experiments. The goal is to describe the logic of causal inference for researchers and policymakers who are not necessarily trained in experimental and quasi-experimental designs and statistical techniques.
Author : Stephen L. Morgan
Publisher : Cambridge University Press
ISBN 13 : 1107065070
Total Pages : 525 pages
Book Rating : 4.1/5 (7 download)
Download or read book Counterfactuals and Causal Inference written by Stephen L. Morgan and published by Cambridge University Press. This book was released on 2015 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.
Author : I. Rohlfing
Publisher : Springer
ISBN 13 : 1137271329
Total Pages : 490 pages
Book Rating : 4.1/5 (372 download)
Download or read book Case Studies and Causal Inference written by I. Rohlfing and published by Springer. This book was released on 2012-09-26 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for improving the case study method and empirical small-n research.
Author : Mika Kivimäki
Publisher : Routledge
ISBN 13 : 1317375122
Total Pages : 536 pages
Book Rating : 4.3/5 (173 download)
Download or read book The Routledge International Handbook of Psychosocial Epidemiology written by Mika Kivimäki and published by Routledge. This book was released on 2017-12-14 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: The health effects of psychosocial factors are a widely discussed and controversial topic. Do positive and negative emotions affect our risk of developing physical disease? Are depressive individuals more likely to have cancer than those with an optimistic outlook on life? And what is the role of IQ in staying healthy and recovering from disease? Importantly, can we improve our health and life expectancy by avoiding certain psychosocial risk factors and maximizing positive psychological well-being? These and other questions are the focus of psychosocial epidemiology, a discipline linking psychological, social and biological sciences. The Routledge International Handbook of Psychosocial Epidemiology is the first book to map this growing discipline. Including contributions from many of the leading researchers in the field, it is divided into five sections: Part I: Methodological challenges in studying psychosocial factors and health; Part II: Psychosocial factors in the etiology and prognosis of chronic diseases; Part III: Controversies in the psychosocial approach; Part IV: Interventions and policy implications Part V: Future research directions Taking advantage of a huge growth in research in recent years, the book provides the reader with the essentials to evaluate the diverse set of studies on psychosocial factors and health that are published today, and describes study designs in this field of research, progress in judging the validity of epidemiological evidence, as well as challenges in translating evidence into action. This is an important and timely book. Providing methodological rigour, critical analysis and the policy implications of this emerging field of study, The Routledge International Handbook of Psychosocial Epidemiology will be an invaluable resource for students and researchers within both behavioural and medical sciences, as well as policy makers and others working in health and social care.
Author : Robert J. Galavan
Publisher : Emerald Group Publishing
ISBN 13 : 1787438422
Total Pages : 367 pages
Book Rating : 4.7/5 (874 download)
Download or read book Methodological Challenges and Advances in Managerial and Organizational Cognition written by Robert J. Galavan and published by Emerald Group Publishing. This book was released on 2017-12-05 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managerial and organizational cognition has sustained and gained the interest of researchers for over a quarter of a century. This volume takes stock of the methodological accomplishments of the MOC field in recent years, and it sets the agenda for the next phase of its development.
Author : Guanglei Hong
Publisher : John Wiley & Sons
ISBN 13 : 1119030609
Total Pages : 443 pages
Book Rating : 4.1/5 (19 download)
Download or read book Causality in a Social World written by Guanglei Hong and published by John Wiley & Sons. This book was released on 2015-06-09 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.
Author : Tyler J. VanderWeele
Publisher : Oxford University Press, USA
ISBN 13 : 0199325871
Total Pages : 729 pages
Book Rating : 4.1/5 (993 download)
Download or read book Explanation in Causal Inference written by Tyler J. VanderWeele and published by Oxford University Press, USA. This book was released on 2015 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.
Author : Judea Pearl
Publisher : John Wiley & Sons
ISBN 13 : 1119186862
Total Pages : 162 pages
Book Rating : 4.1/5 (191 download)
Download or read book Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
Author : Carol S. Aneshensel
Publisher : SAGE
ISBN 13 : 1412994357
Total Pages : 473 pages
Book Rating : 4.4/5 (129 download)
Download or read book Theory-Based Data Analysis for the Social Sciences written by Carol S. Aneshensel and published by SAGE. This book was released on 2013 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.
Author : Gary King
Publisher : Princeton University Press
ISBN 13 : 0691034710
Total Pages : 259 pages
Book Rating : 4.6/5 (91 download)
Download or read book Designing Social Inquiry written by Gary King and published by Princeton University Press. This book was released on 1994-05-22 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?
Author : Judea Pearl
Publisher : Basic Books
ISBN 13 : 0465097618
Total Pages : 432 pages
Book Rating : 4.4/5 (65 download)
Download or read book The Book of Why written by Judea Pearl and published by Basic Books. This book was released on 2018-05-15 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Author : Stephen L. Morgan
Publisher : Cambridge University Press
ISBN 13 : 1316165159
Total Pages : 525 pages
Book Rating : 4.3/5 (161 download)
Download or read book Counterfactuals and Causal Inference written by Stephen L. Morgan and published by Cambridge University Press. This book was released on 2014-11-17 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
