Advertisement
| counterfactuals and causal inference: Elements of Causal Inference Jonas Peters, Dominik Janzing, Bernhard Scholkopf, 2017-11-29 A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts. |
| counterfactuals and causal inference: Counterfactuals and Causal Inference Stephen L. Morgan, Christopher Winship, 2015 This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields. |
| counterfactuals and causal inference: Causal Inference in Statistics Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016-01-25 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. |
| counterfactuals and causal inference: Handbook of Causal Analysis for Social Research Stephen L. Morgan, 2013-04-22 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. |
| counterfactuals and causal inference: Interpretable Machine Learning Christoph Molnar, 2022 Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.--Cover. |
| counterfactuals and causal inference: Counterfactuals and Causal Inference Stephen L. Morgan, Christopher Winship, 2007-07-30 Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual's labor market earnings? Did the use of the butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? If so, was the number of miscast votes sufficiently large to have altered the election outcome? At their core, these types of questions are simple cause-and-effect questions. Simple cause-and-effect questions are the motivation for much empirical work in the social sciences. This book presents a model and set of methods for causal effect estimation that social scientists can use to address causal questions such as these. The essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. |
| counterfactuals and causal inference: Explanation in Causal Inference Tyler VanderWeele, 2015-02-13 The book provides an accessible but comprehensive overview of methods for mediation and interaction. There has been considerable and rapid methodological development on mediation and moderation/interaction analysis within the causal-inference literature over the last ten years. Much of this material appears in a variety of specialized journals, and some of the papers are quite technical. There has also been considerable interest in these developments from empirical researchers in the social and biomedical sciences. However, much of the material is not currently in a format that is accessible to them. The book closes these gaps by providing an accessible, comprehensive, book-length coverage of mediation. The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation and longitudinal data, and relations between mediation and other concepts involving intermediates such as surrogates, principal stratification, instrumental variables, and Mendelian randomization. The second part of the book concerns interaction or moderation, including concepts for interaction, statistical interaction, confounding and interaction, mechanistic interaction, bias analysis for interaction, interaction in genetic studies, and power and sample-size calculation for interaction. The final part of the book provides comprehensive discussion about the relationships between mediation and interaction and unites these concepts within a single framework. This final part also provides an introduction to spillover effects or social interaction, concluding with a discussion of social-network analyses. The book is written to be accessible to anyone with a basic knowledge of statistics. Comprehensive appendices provide more technical details for the interested reader. Applied empirical examples from a variety of fields are given throughout. Software implementation in SAS, Stata, SPSS, and R is provided. The book should be accessible to students and researchers who have completed a first-year graduate sequence in quantitative methods in one of the social- or biomedical-sciences disciplines. The book will only presuppose familiarity with linear and logistic regression, and could potentially be used as an advanced undergraduate book as well. |
| counterfactuals and causal inference: Causality Judea Pearl, 2009-09-14 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 ... |
| counterfactuals and causal inference: An Introduction to Causal Inference , 2009 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. |
| counterfactuals and causal inference: The Effect Nick Huntington-Klein, 2021-12-20 The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do? Key Features: • Extensive code examples in R, Stata, and Python • Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions • An easy-to-read conversational tone • Up-to-date coverage of methods with fast-moving literatures like difference-in-differences |
| counterfactuals and causal inference: On the Edge of Commitment Stephen Lawrence Morgan, 2005 This book offers a new model of educational achievement to explain why some students are committed to preparation for college. |
| counterfactuals and causal inference: Causal Inference in Statistics, Social, and Biomedical Sciences Guido W. Imbens, Donald B. Rubin, 2015-04-06 This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments. |
| counterfactuals and causal inference: The Philosophy of Causality in Economics Mariusz Maziarz, 2020-05-13 Approximately one in six top economic research papers draws an explicitly causal conclusion. But what do economists mean when they conclude that A ‘causes’ B? Does ‘cause’ say that we can influence B by intervening on A, or is it only a label for the correlation of variables? Do quantitative analyses of observational data followed by such causal inferences constitute sufficient grounds for guiding economic policymaking? The Philosophy of Causality in Economics addresses these questions by analyzing the meaning of causal claims made by economists and the philosophical presuppositions underlying the research methods used. The book considers five key causal approaches: the regularity approach, probabilistic theories, counterfactual theories, mechanisms, and interventions and manipulability. Each chapter opens with a summary of literature on the relevant approach and discusses its reception among economists. The text details case studies, and goes on to examine papers which have adopted the approach in order to highlight the methods of causal inference used in contemporary economics. It analyzes the meaning of the causal claim put forward, and finally reconstructs the philosophical presuppositions accepted implicitly by economists. The strengths and limitations of each method of causal inference are also considered in the context of using the results as evidence for policymaking. This book is essential reading to those interested in literature on the philosophy of economics, as well as the philosophy of causality and economic methodology in general. |
| counterfactuals and causal inference: The Book of Why Judea Pearl, Dana Mackenzie, 2018-05-15 The hugely influential book on how the understanding of causality revolutionized science and the world, by the pioneer of artificial intelligence 'Wonderful ... illuminating and fun to read' Daniel Kahneman, Nobel Prize-winner and author of Thinking, Fast and Slow 'Correlation does not imply causation.' For decades, this mantra was invoked by scientists in order to avoid taking positions as to whether one thing caused another, such as smoking and cancer, or carbon dioxide and global warming. But today, that taboo is dead. The causal revolution, sparked by world-renowned computer scientist Judea Pearl and his colleagues, has cut through a century of confusion and placed cause and effect on a firm scientific basis. Now, Pearl and science journalist Dana Mackenzie explain causal thinking to general readers for the first time, showing how it allows us to explore the world that is and the worlds that could have been. It is the essence of human and artificial intelligence. And just as Pearl's discoveries have enabled machines to think better, The Book of Why explains how we too can think better. 'Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term thinking machine' Vint Cerf |
| counterfactuals and causal inference: Counterfactuals and Causal Inference Stephen L. Morgan, Christopher Winship, 2014-11-17 In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed. |
| counterfactuals and causal inference: Making Things Happen James Woodward, 2005-10-27 In Making Things Happen, James Woodward develops a new and ambitious comprehensive theory of causation and explanation that draws on literature from a variety of disciplines and which applies to a wide variety of claims in science and everyday life. His theory is a manipulationist account, proposing that causal and explanatory relationships are relationships that are potentially exploitable for purposes of manipulation and control. This account has its roots in the commonsense idea that causes are means for bringing about effects; but it also draws on a long tradition of work in experimental design, econometrics, and statistics. Woodward shows how these ideas may be generalized to other areas of science from the social scientific and biomedical contexts for which they were originally designed. He also provides philosophical foundations for the manipulationist approach, drawing out its implications, comparing it with alternative approaches, and defending it from common criticisms. In doing so, he shows how the manipulationist account both illuminates important features of successful causal explanation in the natural and social sciences, and avoids the counterexamples and difficulties that infect alternative approaches, from the deductive-nomological model onwards. Making Things Happen will interest philosophers working in the philosophy of science, the philosophy of social science, and metaphysics, and as well as anyone interested in causation, explanation, and scientific methodology. |
| counterfactuals and causal inference: Causation and Counterfactuals John Collins, Ned Hall, L. A. Paul, 2004-06-25 One philosophical approach to causation sees counterfactual dependence as the key to the explanation of causal facts: for example, events c (the cause) and e (the effect) both occur, but had c not occurred, e would not have occurred either. The counterfactual analysis of causation became a focus of philosophical debate after the 1973 publication of the late David Lewis's groundbreaking paper, Causation, which argues against the previously accepted regularity analysis and in favor of what he called the promising alternative of the counterfactual analysis. Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting—or, in some cases, disputing the connection between—counterfactuals and causation, including the complete version of Lewis's Whitehead lectures, Causation as Influence, a major reworking of his original paper. Also included is a more recent essay by Lewis, Void and Object, on causation by omission. Several of the essays first appeared in a special issue of the Journal of Philosophy, but most, including the unabridged version of Causation as Influence, are published for the first time or in updated forms. Other topics considered include the trumping of one event over another in determining causation; de facto dependence; challenges to the transitivity of causation; the possibility that entities other than events are the fundamental causal relata; the distinction between dependence and production in accounts of causation; the distinction between causation and causal explanation; the context-dependence of causation; probabilistic analyses of causation; and a singularist theory of causation. |
| counterfactuals and causal inference: The Grammar of Science Karl Pearson, 2014-12-18 This 1892 publication by an influential mathematician and philosopher of science presents a positivist account of the nature of science. |
| counterfactuals and causal inference: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Agency for Health Care Research and Quality (U.S.), 2013-02-21 This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov) |
| counterfactuals and causal inference: Causal Models Steven Sloman, 2005-07-28 Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. In cognitive terms, how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. How does intervening on one thing affect other things? This is not a question merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation. |
| counterfactuals and causal inference: Philosophical Papers : Volume I David Lewis Professor of Philosophy Princeton University, 1983-06-23 The first volume of this series presents fifteen selected papers dealing with a variety of topics in ontology, philosophy of mind, and philosophy of language. |
| counterfactuals and causal inference: Handbook of Statistical Modeling for the Social and Behavioral Sciences G. Arminger, Clifford C. Clogg, M.E. Sobel, 2013-06-29 Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems. |
| counterfactuals and causal inference: Causation: A Very Short Introduction Stephen Mumford, Rani Lill Anjum, 2013-11-28 Causation is the most fundamental connection in the universe. Without it, there would be no science or technology. There would be no moral responsibility either, as none of our thoughts would be connected with our actions and none of our actions with any consequences. Nor would we have a system of law because blame resides only in someone having caused injury or damage. Any intervention we make in the world around us is premised on there being causal connections that are, to a degree, predictable. It is causation that is at the basis of prediction and also explanation. This Very Short Introduction introduces the key theories of causation and also the surrounding debates and controversies. Do causes produce their effects by guaranteeing them? Do causes have to precede their effects? Can causation be reduced to the forces of physics? And are we right to think of causation as one single thing at all? ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable. |
| counterfactuals and causal inference: The Oxford Handbook of the Development of Imagination Marjorie Taylor, 2013-04-02 Children are widely celebrated for their imaginations, but developmental research on this topic has often been fragmented or narrowly focused on fantasy. However, there is growing appreciation for the role that imagination plays in cognitive and emotional development, as well as its link with children's understanding of the real world. With their imaginations, children mentally transcend time, place, and/or circumstance to think about what might have been, plan and anticipate the future, create fictional relationships and worlds, and consider alternatives to the actual experiences of their lives. The Oxford Handbook of the Development of Imagination provides a comprehensive overview of this broad new perspective by bringing together leading researchers whose findings are moving the study of imagination from the margins of mainstream psychology to a central role in current efforts to understand human thought. The topics covered include fantasy-reality distinctions, pretend play, magical thinking, narrative, anthropomorphism, counterfactual reasoning, mental time travel, creativity, paracosms, imaginary companions, imagination in non-human animals, the evolution of imagination, autism, dissociation, and the capacity to derive real life resilience from imaginative experiences. Many of the chapters include discussions of the educational, clinical, and legal implications of the research findings and special attention is given to suggestions for future research. |
| counterfactuals and causal inference: Impact Evaluation Markus Fröhlich, Markus Frölich, Stefan Sperlich, 2019-03-21 Encompasses the main concepts and approaches of quantitative impact evaluations, used to consider the effectiveness of programmes, policies, projects or interventions. This textbook for economics graduate courses can also serve as a manual for professionals in research institutes, governments, and international organizations. |
| counterfactuals and causal inference: Causality Carlo Berzuini, Philip Dawid, Luisa Bernardinell, 2012-08-13 A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book. |
| counterfactuals and causal inference: Causal Inference Miguel A. Hernan, James M Robins, 2023-08 Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data-- |
| counterfactuals and causal inference: Cognitive Systems Henrik Christensen, Geert-Jan M. Kruijff, Jeremy L. Wyatt, 2010-04-05 Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application – exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program. |
| counterfactuals and causal inference: Causal Inference Scott Cunningham, 2021-01-26 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. |
| counterfactuals and causal inference: Practice Learning and Teaching Mark Doel, Steven Shardlow, 1996-11-11 Recognising the centrality of the practice placement in preparing students to become effective social workers, this book offers practical guidance to both students and their practice teachers regarding how to enhance learning on placement in social work agencies. Distinguished by the coherence of its approach, the book presents an integrated approach to practice teaching with a clear methodological focus, practical help for practitioners and discussion of principles for students. |
| counterfactuals and causal inference: Foundations of Bayesianism D. Corfield, J. Williamson, 2013-03-14 Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. Some of these papers seek to clarify the relationships between Bayesian, causal and logical reasoning. Others consider the application of Bayesianism to artificial intelligence, decision theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. The upshot is a plethora of new problems and directions for Bayesians to pursue. The book will be of interest to graduate students or researchers who wish to learn more about Bayesianism than can be provided by introductory textbooks to the subject. Those involved with the applications of Bayesian reasoning will find essential discussion on the validity of Bayesianism and its limits, while philosophers and others interested in pure reasoning will find new ideas on normativity and the logic of belief. |
| counterfactuals and causal inference: What Might Have Been Neal J. Roese, James M. Olson, 2014-01-14 Within a few short years, research on counterfactual thinking has mushroomed, establishing itself as one of the signature domains within social psychology. Counterfactuals are thoughts of what might have been, of possible past outcomes that could have taken place. Counterfactuals and their implications for perceptions of time and causality have long fascinated philosophers, but only recently have social psychologists made them the focus of empirical inquiry. Following the publication of Kahneman and Tversky's seminal 1982 paper, a burgeoning literature has implicated counterfactual thinking in such diverse judgments as causation, blame, prediction, and suspicion; in such emotional experiences as regret, elation, disappointment and sympathy; and also in achievement, coping, and intergroup bias. But how do such thoughts come about? What are the mechanisms underlying their operation? How do their consequences benefit, or harm, the individual? When is their generation spontaneous and when is it strategic? This volume explores these and other numerous issues by assembling contributions from the most active researchers in this rapidly expanding subfield of social psychology. Each chapter provides an in-depth exploration of a particular conceptual facet of counterfactual thinking, reviewing previous work, describing ongoing, cutting-edge research, and offering novel theoretical analysis and synthesis. As the first edited volume to bring together the many threads of research and theory on counterfactual thinking, this book promises to be a source of insight and inspiration for years to come. |
| counterfactuals and causal inference: The Art of Causal Conjecture Glenn Shafer, 1996 In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy. The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences -- medicine, business, engineering, and artificial intelligence -- must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences. Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence. |
| counterfactuals and causal inference: Multivariate Dependencies D.R. Cox, Nanny Wermuth, 2014-07-22 Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more technical statistical issues involved in analysis and interpretation. Numerous illustrative examples ar |
| counterfactuals and causal inference: Handbook of Structural Equation Modeling Rick H. Hoyle, 2023-02-17 This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results-- |
| counterfactuals and causal inference: Actual Causality Joseph Y. Halpern, 2019-02-19 A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation. Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume. In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression. Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification. Technical details are generally confined to the final section of each chapter and can be skipped by non-mathematical readers. |
| counterfactuals and causal inference: Memory and Mind Mark A. Gluck, John R. Anderson, Stephen M. Kosslyn, 2007-09-07 A comprehensive overview of the current state of research on memory and mind, this book captures the career and influence of Gordon H. Bower (as told by 22 of his students and colleagues), showing how Bower's research and mentoring of students has broadly and deeply affected modern research. In addition to many personal reminisces about Bower's res |
| counterfactuals and causal inference: Identification for Prediction and Decision Charles F. Manski, 2009-06-30 This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements. Building on the foundation laid in the author's Identification Problems in the Social Sciences (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behavior. Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory. |
| counterfactuals and causal inference: Multi-Method Social Science Jason Seawright, 2016-09-08 Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation. |
| counterfactuals and causal inference: Statistics in the 21st Century Martin A. Tanner, 2019-10-02 This volume discusses an important area of statistics and highlights the most important statistical advances. It is divided into four sections: statistics in the life and medical sciences, business and social science, the physical sciences and engineering, and theory and methods of statistics. |
Counterfactuals - Stanford Encyclopedia of Philosophy
Jan 18, 2019 · It will aim to refine this question, explain its central role in certain philosophical debates, and outline the main semantic analyses of counterfactuals.
Counterfactual thinking - Wikipedia
Counterfactual thinking is a concept in psychology that involves the human tendency to create possible alternatives to life events that have already occurred; something that is contrary to …
COUNTERFACTUALS AND CAUSAL REASONING
Counterfactuals serve as a convenient tool for stating certain intermediate conclusions in this reasoning procedure, and that is one of the reasons why we have a counterfactual construction.
Counterfactual - Definition and examples — Conceptually
Counterfactual reasoning means thinking about alternative possibilities for past or future events: what might happen/ have happened if…? In other words, you imagine the consequences of …
Counterfactuals and Their Applications
we can use SEMs to define what counterfactuals stand for, how to read counterfactuals from a given model, and how probabilities of counterfactuals can be estimated when portions of the …
Computational basis of hierarchical and counterfactual ...
6 days ago · An analysis of the counterfactual model indicated that counterfactuals are relied upon more often when the first decision is more difficult, that is, the difference between the two …
Counterfactuals - A Political Science Guide
Effective counterfactuals are bound by specific, logical criteria. Joseph Nye spells out four important elements that go into the production of a credible counter-factual: plausability, …
Counterfactuals - Stanford Encyclopedia of Philosophy
Jan 18, 2019 · It will aim to refine this question, explain its central role in certain philosophical debates, and outline the main semantic analyses of counterfactuals.
Counterfactual thinking - Wikipedia
Counterfactual thinking is a concept in psychology that involves the human tendency to create possible alternatives to life events that have already occurred; something that is contrary to …
COUNTERFACTUALS AND CAUSAL REASONING - Princeton …
Counterfactuals serve as a convenient tool for stating certain intermediate conclusions in this reasoning procedure, and that is one of the reasons why we have a counterfactual construction.
Counterfactual - Definition and examples — Conceptually
Counterfactual reasoning means thinking about alternative possibilities for past or future events: what might happen/ have happened if…? In other words, you imagine the consequences of …
Counterfactuals and Their Applications
we can use SEMs to define what counterfactuals stand for, how to read counterfactuals from a given model, and how probabilities of counterfactuals can be estimated when portions of the …
Computational basis of hierarchical and counterfactual ...
6 days ago · An analysis of the counterfactual model indicated that counterfactuals are relied upon more often when the first decision is more difficult, that is, the difference between the two …
Counterfactuals - A Political Science Guide
Effective counterfactuals are bound by specific, logical criteria. Joseph Nye spells out four important elements that go into the production of a credible counter-factual: plausability, …
