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This anthology of articles on causal inference and scientific paradigms in epidemiology covers several important topics including the search for causal explanations, the strengths and limitations of causal criteria, quantitative approaches for assessing causal relationships that are relevant to epidemiology and emerging paradigms in epidemiologic research. In order to provide historical context, an overview of philosophical and historical developments relevant to causal inference in epidemiology and public health is also provided. Several theoretical and applied aspects of causal inference are dealt with. The aim of this Ebook is not only to summarize important developments in causal inference in epidemiology but also to identify possible ways to enhance the search for causal explanations for diseases and injuries. Examples are provided from such fields as chronic disease epidemiology, Veterans health, and environmental epidemiology. A particular goal of the Ebook is to provide ideas for strengthening causal inference in epidemiology in the context of refined research paradigms. These topics are important because the results of epidemiologic studies contribute to generalizable knowledge by clarifying the causes of diseases, by combining epidemiologic data with information from other disciplines (for example, psychology and industrial hygiene), by evaluating the consistency of epidemiologic data with etiological hypotheses about causation, and by providing the basis for evaluating procedures for health promotion and prevention and public health practices.
First edition published in 2002. Second edition published in 2008.
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.
The second edition of this essential introduction to epidemiology presents the core concepts in a unified approach that aims to cut through the fog and elucidate the fundamental concepts.
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.
Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions.
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.
At its core, epidemiology is concerned with changes in health and disease. The discipline requires counts and measures: of births, health disorders, and deaths, and in order to make sense of these counts it requires a population base defined by place and time. Epidemiology relies on closely defined concepts of cause - experimental or observational - of the physical or social environment, or in the laboratory. Epidemiologists are guided by these concepts, and have often contributed to their development. Because the disciplinary focus is on health and disease in populations, epidemiology has always been an integral driver of public health, the vehicle that societies have evolved to combat and contain the scourges of mass diseases.In this book, the authors trace the evolution of epidemiological ideas from earliest times to the present. Beginning with the early concepts of magic and the humors of Hippocrates, it moves forward through the dawn of observational methods, the systematic counts of deaths initiated in 16th-century London by John Graunt and William Petty, the late 18th-century Enlightenment and the French Revolution, which established the philosophical argument for health as a human right, the national public health system begun in 19th-century Britain, up to the development of eco-epidemiology, which attempts to re-integrate the fragmented fields as they currently exist. By examining the evolution of epidemiology as it follows the evolution of human societies, this book provides insight into our shared intellectual history and shows a way forward for future study.
Winner of the 2011 BMA book awards: medicine categoryIn the five decades since its first publication, Hunter's Diseases of Occupations has remained the pre-eminent text on diseases caused by work, universally recognized as the most authoritative source of information in the field. It is an important guide for doctors in all disciplines who may
To date, much of the empirical work in social epidemiology has demonstrated the existence of health inequalities along a number of axes of social differentiation. However, this research, in isolation, will not inform effective solutions to health inequalities. Rethinking Social Epidemiology provides an expanded vision of social epidemiology as a science of change, one that seeks to better address key questions related to both the causes of social inequalities in health (problem-focused research) as well as the implementation of interventions to alleviate conditions of marginalization and poverty (solution-focused research). This book is ideally suited for emerging and practicing social epidemiologists as well as graduate students and health professionals in related disciplines.