Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applications Key Features Explore causal analysis with hands-on R tutorials and real-world examples Grasp complex statistical methods by taking a detailed, easy-to-follow approach Equip yourself with actionable insights and strategies for making data-driven decisions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDetermining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making. This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You’ll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You’ll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data. By the end of this book, you’ll be able to confidently establish causal relationships and make data-driven decisions with precision.What you will learn Get a solid understanding of the fundamental concepts and applications of causal inference Utilize R to construct and interpret causal models Apply techniques for robust causal analysis in real-world data Implement advanced causal inference methods, such as instrumental variables and propensity score matching Develop the ability to apply graphical models for causal analysis Identify and address common challenges and pitfalls in controlled experiments for effective causal analysis Become proficient in the practical application of doubly robust estimation using R Who this book is for This book is for data practitioners, statisticians, and researchers keen on enhancing their skills in causal inference using R, as well as individuals who aspire to make data-driven decisions in complex scenarios. It serves as a valuable resource for both beginners and experienced professionals in data analysis, public policy, economics, and social sciences. Academics and students looking to deepen their understanding of causal models and their practical implementation will also find it highly beneficial.

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.

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.

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.

How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution

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.

This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.

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.

This book examines how legal causation inference and epidemiological causal inference can be harmonized within the realm of jurisprudence, exploring why legal causation and epidemiological causation differ from each other and defining related problems. The book also discusses how legal justice can be realized and how victims’ rights can be protected. It looks at epidemiological evidence pertaining to causal relationships in cases such as smoking and the development of lung cancer, and enables readers to correctly interpret and rationally use the results of epidemiological studies in lawsuits. The book argues that in today’s risk society, it is no longer possible to thwart the competence of evidence using epidemiological research results. In particular, it points out that the number of cases that struggle to prove a causal relationship excluding those using epidemiological data will lead to an increase in the number of lawsuits for damages that arise as a result of harmful materials that affect our health. The book argues that the responsibility to compensate for damages that have actually occurred must be imputed to a particular party and that this can be achieved by understanding causal inferences between jurisprudence and epidemiology. This book serves as a foundation for students, academics and researchers who have an interest in epidemiology and the law, and those who are keen to discover how jurisprudence can bring these two areas together.