Causality and Causal Modelling in the Social Sciences


Book Description

This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science.




Handbook of Causal Analysis for Social Research


Book Description

What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.




Causal Inference


Book Description

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.




Causal Inference in Statistics, Social, and Biomedical Sciences


Book Description

This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.




Mechanisms and the Contingency of Social Causality


Book Description

Mechanisms are frequently brought up across the natural and social sciences as supplements to laws and empirical regularities. Recent decades have seen an explosion in mechanistic explanations in which philosophers of science, natural scientists, and social scientists have advocated, debated, and criticized the usage of mechanisms in their respective disciplines. As the intensity of these debates has increased, our understanding of the historical origin of mechanisms remains incomplete. Of the explanations that have been put forward, it has been argued that the roots of mechanisms are to be found in mechanical philosophy. This book demonstrates that an important set of factors have been overlooked in our understanding of the ontology of mechanisms. In shifting attention to a never-before-explored terrain in the etymological and semantic evolution of what arguably is the most commonly used scientific term, “the mechanism,” this text discovers that the origin of mechanisms is to be witnessed in ideas about social causality that arose within Ancient Greek tragedy and theater. It takes readers on a journey through socio-cultural settings and changes in Ancient Greece, early Christianity, the Roman Empire, and the Middle Ages, as well as the rise of science and modernity, and finishes in our current era of digital technology. As such, the book reveals how understandings of mechanisms have changed and evolved across time.




Causality in a Social World


Book Description

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.




Causal Models in the Social Sciences


Book Description

Causal models are formal theories stating the relationships between precisely defined variables, and have become an indispensable tool of the social scientist. This collection of articles is a course book on the causal modeling approach to theory construction and data analysis. H. M. Blalock, Jr. summarizes the then-current developments in causal model utilization in sociology, political science, economics, and other disciplines. This book provides a comprehensive multidisciplinary picture of the work on causal models. It seeks to address the problem of measurement in the social sciences and to link theory and research through the development of causal models.Organized into five sections (Simple Recursive Models, Path Analysis, Simultaneous Equations Techniques, The Causal Approach to Measurement Error, and Other Complications), this volume contains twenty-seven articles (eight of which were specially commissioned). Each section begins with an introduction explaining the concepts to be covered in the section and links them to the larger subject. It provides a general overview of the theory and application of causal modeling.Blalock argues for the development of theoretical models that can be operationalized and provide verifiable predictions. Many of the discussions of this subject that occur in other literature are too technical for most social scientists and other scholars who lack a strong background in mathematics. This book attempts to integrate a few of the less technical papers written by econometricians such as Koopmans, Wold, Strotz, and Fisher with discussions of causal approaches in the social and biological sciences. This classic text by Blalock is a valuable source of material for those interested in the issue of measurement in the social sciences and the construction of mathematical models.




Causality


Book Description

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 ...




Questioning Causality


Book Description

Covering a topic applicable to fields ranging from education to health care to psychology, this book provides a broad critical analysis of the assumptions that researchers and practitioners have about causation and explains how readers can improve their thinking about causation. In virtually every laboratory, research center, or classroom focused on the social or physical sciences today, the concept of causation is a core issue to be questioned, tested, and determined. Even debates in unrelated areas such as biology, law, and philosophy often focus on causality—"What made that happen?" In this book, experts from across disciplines adopt a reader-friendly approach to reconsider this age-old question in a modern light, defining different kinds of causation and examining how causes and consequences are framed and approached in a particular field. Each chapter uses applied examples to illustrate key points in an accessible manner. The contributors to this work supply a coherent critical analysis of the assumptions researchers and practitioners hold about causation, and explain how such thinking about causation can be improved. Collectively, the coverage is broad, providing readers with a fuller picture of research in social contexts. Beyond providing insightful description and thought-provoking questioning of causation in different research areas, the book applies analysis of data in order to point the way to smarter, more efficient practices. Consequently, both practitioners and researchers will benefit from this book.




Quantitative Social Science


Book Description

"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--