Download or read book Demystifying Causal Inference written by Vikram Dayal and published by Springer Nature. This book was released on 2023-09-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snow’s heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.

Download or read book Demystifying Causal Inference written by Vikram Dayal and published by Springer. This book was released on 2024-10-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snow's heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with thehelp of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.

Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Download or read book Causal Inference in Statistics Social and Biomedical Sciences written by Guido W. Imbens and published by Cambridge University Press. This book was released on 2015-04-06 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Download or read book Foundations of Agnostic Statistics written by Peter M. Aronow and published by Cambridge University Press. This book was released on 2019-01-31 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an introduction to modern statistical theory for social and health scientists while invoking minimal modeling assumptions.

Download or read book Analyzing Quantitative Data written by Norman Blaikie and published by SAGE. This book was released on 2003-03-06 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: For social researchers who need to know what procedures to use under what circumstances in practical research projects, this book does not require an indepth understanding of statistical theory.

Download or read book Narration Identity and Historical Consciousness written by Jürgen Straub and published by Berghahn Books. This book was released on 2005 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: A generally acknowledged characteristic of modern life, namely the temporalization of experience, inextricable from our intensified experience of contingency and difference, has until now remained largely outside psychology's purview. Wherever questions about the development, structure, and function of the concept of time have been posed - for example by Piaget and other founders of genetic structuralism - they have been concerned predominantly with concepts of "physical", chronometrical time, and related concepts (e.g., "velocity"). All the contributions to the present volume attempt to close this gap. A larger number are especially interested in the narration of stories. Overviews of the relevant literature, as well as empirical case studies, appear alongside theoretical and methodological reflections. Most contributions refer to specifically historical phenomena and meaning-constructions. Some touch on the subjects of biographical memory and biographical constructions of reality. Of all the various affinities between the contributions collected here, the most important is their consistent attention to issues of the constitution and representation of temporal experience.

Download or read book Statistical Causal Inferences and Their Applications in Public Health Research written by Hua He and published by Springer. This book was released on 2016-10-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

Download or read book Semiparametric Theory and Missing Data written by Anastasios Tsiatis and published by Springer Science & Business Media. This book was released on 2007-01-15 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

Download or read book Good and Real written by Gary L. Drescher and published by MIT Press. This book was released on 2006 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examining a series of provocative paradoxes about consciousness, choice, ethics, and other topics, Good and Real tries to reconcile a purely mechanical view of the universe with key aspects of our subjective impressions of our own existence. In Good and Real, Gary Drescher examines a series of provocative paradoxes about consciousness, choice, ethics, quantum mechanics, and other topics, in an effort to reconcile a purely mechanical view of the universe with key aspects of our subjective impressions of our own existence. Many scientists suspect that the universe can ultimately be described by a simple (perhaps even deterministic) formalism; all that is real unfolds mechanically according to that formalism. But how, then, is it possible for us to be conscious, or to make genuine choices? And how can there be an ethical dimension to such choices? Drescher sketches computational models of consciousness, choice, and subjunctive reasoning--what would happen if this or that were to occur? --to show how such phenomena are compatible with a mechanical, even deterministic universe. Analyses of Newcomb's Problem (a paradox about choice) and the Prisoner's Dilemma (a paradox about self-interest vs. altruism, arguably reducible to Newcomb's Problem) help bring the problems and proposed solutions into focus. Regarding quantum mechanics, Drescher builds on Everett's relative-state formulation--but presenting a simplified formalism, accessible to laypersons--to argue that, contrary to some popular impressions, quantum mechanics is compatible with an objective, deterministic physical reality, and that there is no special connection between quantum phenomena and consciousness. In each of several disparate but intertwined topics ranging from physics to ethics, Drescher argues that a missing technical linchpin can make the quest for objectivity seem impossible, until the elusive technical fix is at hand.

Download or read book Understanding Political Science Research Methods written by Maryann Barakso and published by Routledge. This book was released on 2013-12-04 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text starts by explaining the fundamental goal of good political science research—the ability to answer interesting and important questions by generating valid inferences about political phenomena. Before the text even discusses the process of developing a research question, the authors introduce the reader to what it means to make an inference and the different challenges that social scientists face when confronting this task. Only with this ultimate goal in mind will students be able to ask appropriate questions, conduct fruitful literature reviews, select and execute the proper research design, and critically evaluate the work of others. The authors' primary goal is to teach students to critically evaluate their own research designs and others’ and analyze the extent to which they overcome the classic challenges to making inference: internal and external validity concerns, omitted variable bias, endogeneity, measurement, sampling, and case selection errors, and poor research questions or theory. As such, students will not only be better able to conduct political science research, but they will also be more savvy consumers of the constant flow of causal assertions that they confront in scholarship, in the media, and in conversations with others. Three themes run through Barakso, Sabet, and Schaffner’s text: minimizing classic research problems to making valid inferences, effective presentation of research results, and the nonlinear nature of the research process. Throughout their academic years and later in their professional careers, students will need to effectively convey various bits of information. Presentation skills gleaned from this text will benefit students for a lifetime, whether they continue in academia or in a professional career. Several distinctive features make this book noteworthy: A common set of examples threaded throughout the text give students a common ground across chapters and expose them to a broad range of subfields in the discipline. Box features throughout the book illustrate the nonlinear, "non-textbook" reality of research, demonstrate the often false inferences and poor social science in the way the popular press covers politics, and encourage students to think about ethical issues at various stages of the research process.

Download or read book Causality written by Judea Pearl and published by Cambridge University Press. This book was released on 2009-09-14 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...

Download or read book Principles of Methodology written by Perri 6 and published by SAGE. This book was released on 2011-10-17 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive, accessible guide to social science methodology. In so doing, it establishes methodology as distinct from both methods and philosophy. Most existing textbooks deal with methods, or sound ways of collecting and analysing data to generate findings. In contrast, this innovative book shows how an understanding of methodology allows us to design research so that findings can be used to answer interesting research questions and to build and test theories. Most important things in social research (e.g., beliefs, institutions, interests, practices and social classes) cannot be observed directly. This book explains how empirical research can nevertheless be designed to make sound inferences about their nature, effects and significance. The authors examine what counts as good description, explanation and interpretation, and how they can be achieved by striking intelligent trade-offs between competing design virtues. Coverage includes: • why methodology matters; • what philosophical arguments show us about inference; • competing virtues of good research design; • purposes of theory, models and frameworks; • forming researchable concepts and typologies; • explaining and interpreting: inferring causation, meaning and significance; and • combining explanation and interpretation. The book is essential reading for new researchers faced with the practical challenge of designing research. Extensive examples and exercises are provided, based on the authors′ long experience of teaching methodology to multi-disciplinary groups. Perri 6 is Professor of Social Policy in the Graduate School in the College of Business, Law and Social Sciences at Nottingham Trent University. Chris Bellamy is Emeritus Professor of Public Administration in the Graduate School, Nottingham Trent University.

Download or read book Python Deep Learning Projects written by Matthew Lamons and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Insightful projects to master deep learning and neural network architectures using Python and Keras Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

Download or read book Unified Methods for Censored Longitudinal Data and Causality written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2012-11-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. students. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.

Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.