Case Studies And Causal Inference Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Case Studies And Causal Inference book. This book definitely worth reading, it is an incredibly well-written.
Case Studies and Causal Inference by I. Rohlfing Pdf
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.
Case Studies and Causal Inference by I. Rohlfing Pdf
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.
Multimethod Research, Causal Mechanisms, and Case Studies by Gary Goertz Pdf
An innovative and accessible textbook on multimethod and case-study research Multimethod research has become indispensable to doing social science, and is essential to anyone who conducts large-scale research projects in political science, sociology, education, comparative law, or business. This authoritative and accessible book offers the first truly comprehensive approach to multimethod and case-study research, and is particularly aimed at students of qualitative methods in the social sciences. Walking step-by-step through these cutting-edge tools and techniques, Gary Goertz introduces a new integrated approach that unites three corners of a powerful research triad—causal mechanisms, cross-case causal inference, and within-case causal inference. He explains how the investigation of causal mechanisms and the making of within-case causal inference are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis with cross-case analysis, whether they are statistical analyses, experiments, or QCA. In addition, Goertz analyzes how one can generalize using case studies, as well as systematically test game-theoretic and other models using multiple case studies. Provides a fully integrated approach to multimethod and case-study research An essential resource for students and researchers in political science, sociology, education, law, and business Covers constraint causal mechanism, game theory and case studies, QCA, and the use of case studies to systematically test and generalize theories An ideal textbook for a first-year graduate course in methods or research design
Comparative Policy Studies by I. Engeli,C. Rothmayr Allison,Christine Rothmayr Allison Pdf
In the first volume of its kind, a collection of top policy scholars combine empirical and methodological analysis in the field of comparative policy studies to provide compelling insights into the formulation, implementation and evaluation of policies across regional and national boundaries.
Elements of Causal Inference by Jonas Peters,Dominik Janzing,Bernhard Scholkopf Pdf
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.
Designing Social Inquiry by Gary King,Robert O. Keohane,Sidney Verba Pdf
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?
Case Studies and Causal Inference by I. Rohlfing Pdf
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.
The Case for Case Studies by Jennifer Widner,Michael Woolcock,Daniel Ortega Nieto Pdf
This volume demonstrates how to conduct case study research that is both methodologically rigorous and useful to development policy. It will interest scholars and students across the social sciences using case studies, and provide constructive guidance to practitioners in development and public administration.
An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences "Causation versus correlation has been the basis of arguments--economic and otherwise--since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It's rare that a book prompts readers to expand their outlook; this one did for me."--Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied--for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
Designing Case Studies by J. Blatter,M. Haverland Pdf
The authors explore three ways of conducting causal analysis in case studies. They draw on established practices as well as on recent innovations in case study methodology and integrate these insights into coherent approaches. They highlight the core features of each approach and provide advice on each step of the research process.
Author : Mark J. van der Laan,Sherri Rose Publisher : Springer Science & Business Media Page : 628 pages File Size : 44,6 Mb Release : 2011-06-17 Category : Mathematics ISBN : 9781441997821
Targeted Learning by Mark J. van der Laan,Sherri Rose Pdf
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.
Author : Mark J. van der Laan,Sherri Rose Publisher : Springer Page : 640 pages File Size : 43,5 Mb Release : 2018-03-28 Category : Mathematics ISBN : 9783319653044
Targeted Learning in Data Science by Mark J. van der Laan,Sherri Rose Pdf
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.
