[PDF] Demystifying Causal Inference eBook

Author : Vikram Dayal
Publisher : Springer Nature
Page : 304 pages
File Size : 31,77 MB
Release : 2023-09-29
Category : Business & Economics
ISBN : 9819939054

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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.

Author : Guanglei Hong
Publisher : John Wiley & Sons
Page : 443 pages
File Size : 40,24 MB
Release : 2015-06-09
Category : Mathematics
ISBN : 1119030609

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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.

Author : Tyler J. VanderWeele
Publisher : Oxford University Press, USA
Page : 729 pages
File Size : 40,69 MB
Release : 2015
Category : Mathematics
ISBN : 0199325871

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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.

Author : Judea Pearl
Publisher : Createspace Independent Publishing Platform
Page : 0 pages
File Size : 45,75 MB
Release : 2015
Category : Causation
ISBN : 9781507894293

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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.

Author : Guido W. Imbens
Publisher : Cambridge University Press
Page : 647 pages
File Size : 44,16 MB
Release : 2015-04-06
Category : Business & Economics
ISBN : 0521885884

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

Author : Judea Pearl
Publisher : John Wiley & Sons
Page : 162 pages
File Size : 11,91 MB
Release : 2016-01-25
Category : Mathematics
ISBN : 1119186862

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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.

Author : Norman Blaikie
Publisher : SAGE
Page : 376 pages
File Size : 36,79 MB
Release : 2003-03-06
Category : Language Arts & Disciplines
ISBN : 9780761967583

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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.

Author : Peter Burke
Publisher : Polity
Page : 236 pages
File Size : 31,90 MB
Release : 2005
Category : History
ISBN : 0745634079

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Taking into account new developments since this book was first published, 'History and Social Theory' discusses topics including globalization, postcolonialism and social capital.

Author : Peter M. Aronow
Publisher : Cambridge University Press
Page : 317 pages
File Size : 47,36 MB
Release : 2019-01-31
Category : Mathematics
ISBN : 1107178916

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Provides an introduction to modern statistical theory for social and health scientists while invoking minimal modeling assumptions.