Demystifying Causal Inference

Demystifying Causal Inference

Author: Vikram Dayal

Publisher: Springer Nature

Published: 2023-09-29

Total Pages: 304

ISBN-13: 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.


Causal Inference in Statistics

Causal Inference in Statistics

Author: Judea Pearl

Publisher: John Wiley & Sons

Published: 2016-01-25

Total Pages: 162

ISBN-13: 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.


An Introduction to Causal Inference

An Introduction to Causal Inference

Author: Judea Pearl

Publisher: Createspace Independent Publishing Platform

Published: 2015

Total Pages: 0

ISBN-13: 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.


Foundations of Agnostic Statistics

Foundations of Agnostic Statistics

Author: Peter M. Aronow

Publisher: Cambridge University Press

Published: 2019-01-31

Total Pages: 317

ISBN-13: 1107178916

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


Semiparametric Theory and Missing Data

Semiparametric Theory and Missing Data

Author: Anastasios Tsiatis

Publisher: Springer Science & Business Media

Published: 2007-01-15

Total Pages: 392

ISBN-13: 0387373454

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This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.


Analyzing Quantitative Data

Analyzing Quantitative Data

Author: Norman Blaikie

Publisher: SAGE

Published: 2003-03-06

Total Pages: 380

ISBN-13: 9780761967590

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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.


Narration, Identity, and Historical Consciousness

Narration, Identity, and Historical Consciousness

Author: Jürgen Straub

Publisher: Berghahn Books

Published: 2005

Total Pages: 300

ISBN-13: 9781845450397

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A generally acknowledged characteristic of modern life, namely the temporalization of experience, inextricable from our intensified experience of contingency and difference, has until now remained largely outside psychology's purview. Wherever questions about the development, structure, and function of the concept of time have been posed - for example by Piaget and other founders of genetic structuralism - they have been concerned predominantly with concepts of "physical", chronometrical time, and related concepts (e.g., "velocity"). All the contributions to the present volume attempt to close this gap. A larger number are especially interested in the narration of stories. Overviews of the relevant literature, as well as empirical case studies, appear alongside theoretical and methodological reflections. Most contributions refer to specifically historical phenomena and meaning-constructions. Some touch on the subjects of biographical memory and biographical constructions of reality. Of all the various affinities between the contributions collected here, the most important is their consistent attention to issues of the constitution and representation of temporal experience.


Good and Real

Good and Real

Author: Gary L. Drescher

Publisher: MIT Press

Published: 2006

Total Pages: 365

ISBN-13: 0262042339

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Examining a series of provocative paradoxes about consciousness, choice, ethics, and other topics, Good and Real tries to reconcile a purely mechanical view of the universe with key aspects of our subjective impressions of our own existence. In Good and Real, Gary Drescher examines a series of provocative paradoxes about consciousness, choice, ethics, quantum mechanics, and other topics, in an effort to reconcile a purely mechanical view of the universe with key aspects of our subjective impressions of our own existence. Many scientists suspect that the universe can ultimately be described by a simple (perhaps even deterministic) formalism; all that is real unfolds mechanically according to that formalism. But how, then, is it possible for us to be conscious, or to make genuine choices? And how can there be an ethical dimension to such choices? Drescher sketches computational models of consciousness, choice, and subjunctive reasoning--what would happen if this or that were to occur? --to show how such phenomena are compatible with a mechanical, even deterministic universe. Analyses of Newcomb's Problem (a paradox about choice) and the Prisoner's Dilemma (a paradox about self-interest vs. altruism, arguably reducible to Newcomb's Problem) help bring the problems and proposed solutions into focus. Regarding quantum mechanics, Drescher builds on Everett's relative-state formulation--but presenting a simplified formalism, accessible to laypersons--to argue that, contrary to some popular impressions, quantum mechanics is compatible with an objective, deterministic physical reality, and that there is no special connection between quantum phenomena and consciousness. In each of several disparate but intertwined topics ranging from physics to ethics, Drescher argues that a missing technical linchpin can make the quest for objectivity seem impossible, until the elusive technical fix is at hand.


Statistical Causal Inferences and Their Applications in Public Health Research

Statistical Causal Inferences and Their Applications in Public Health Research

Author: Hua He

Publisher: Springer

Published: 2016-10-26

Total Pages: 324

ISBN-13: 3319412590

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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.