Counterfactuals is David Lewis's forceful presentation of and sustained argument for a particular view about propositions which express contrary-to-fact conditionals, including his famous defense of realism about possible worlds. Since its original publication in 1973, it has become a classic of contemporary philosophy, and is essential reading for anyone interested in the logic and metaphysics of counterfactuals. The book also includes an appendix of related writings by Lewis.

Emerging as a hot topic in the mid-twentieth century, causality is one of the most frequently discussed issues in contemporary philosophy. Causality has been a central concept in philosophy as well as in the sciences, especially the natural sciences, dating back to its beginning in Greek thought. David Hume famously claimed that causality is the cement of the universe. In general terms, it links eventualities, predicts the consequences of action, and is the cognitive basis for the acquisition and the use of categories and concepts in the child. Indeed, how could one answer why-questions, around which early rational thought begins to revolve, without hitting on the relationships between reason and consequence, cause and effect, or without drawing these distinctions? But a comprehensive definition of causality has been notoriously hard to provide, and virtually every aspect of causation has been subject to much debate and analysis. "Thinking about Causes" brings together top philosophers from the United States and Europe to focus on causality as a major force in philosophical and scientific thought. Topics addressed include: ancient Stoicism and moral philosophy; the case of sacramental causality; traditional causal concepts in Descartes; Kant on transcendental laws; the influence of J. S. Mill's politics on his concept of causation; plurality in causality; causality in modern physics; causality in economics; and the concept of free will. Taken together, the essays in this collection provide the best current thinking about causality, especially as it relates to the philosophy of science.

Only human beings have a rich conceptual repertoire with concepts like tort, entropy, Abelian group, mannerism, icon and deconstruction. How have humans constructed these concepts? And once they have been constructed by adults, how do children acquire them? While primarily focusing on the second question, in The Origin of Concepts , Susan Carey shows that the answers to both overlap substantially.
Carey begins by characterizing the innate starting point for conceptual development, namely systems of core cognition. Representations of core cognition are the output of dedicated input analyzers, as with perceptual representations, but these core representations differ from perceptual representations in having more abstract contents and richer functional roles. Carey argues that the key to understanding cognitive development lies in recognizing conceptual discontinuities in which new representational systems emerge that have more expressive power than core cognition and are also incommensurate with core cognition and other earlier representational systems. Finally, Carey fleshes out Quinian bootstrapping, a learning mechanism that has been repeatedly sketched in the literature on the history and philosophy of science. She demonstrates that Quinian bootstrapping is a major mechanism in the construction of new representational resources over the course of childrens cognitive development.
Carey shows how developmental cognitive science resolves aspects of long-standing philosophical debates about the existence, nature, content, and format of innate knowledge. She also shows that understanding the processes of conceptual development in children illuminates the historical process by which concepts are constructed, and transforms the way we think about philosophical problems about the nature of concepts and the relations between language and thought.

This volume explains recent theoretical developments in the econometric modelling of relationships between different statistical series. The statistical techniques explored analyse relationships between different variables, over time, such as the relationship between variables in a macroeconomy. Examples from Professor Terasvirta's empirical work are given. Professors Granger and Terasvirta are leading exponents of techniques of dynamic, multivariate analysis. They illustrate in this volume exploratory ways of using such techniques to provide models of nonlinear relationships between variables. This is an extension of previous work on linear relationships, and on univariate models. These developments will be of use to econometricians wishing to construct and use models of nonlinear, dynamic, multivariate relationships, such as an investment function, or a production function. Particular attention is paid to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. The book concentrates on stochastic series, since the existence of unexpected shocks strongly suggests that economic variables are stochastic. Granger and Terasvirta also discuss the division of these nonlinear relationships into parametric and nonparametric models.

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

The History of Econometric Ideas covers the period from the late nineteenth century to the middle of the twentieth century, illustrating how economists first learned to harness statistical methods to measure and test the "laws" of economics. Though scholarly, Dr. Morgan's book is very accessible; it does not require a high level of prior statistical knowledge, and will be of interest to practicing statisticians and economists.

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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 are described in outline and four studies are discussed in some detail. The use of graphical representations of dependencies and independencies among the features under study is stressed, both to incorporate available knowledge at the planning stage of an analysis and to summarize aspects important for interpretation after detailed statistical analysis is complete. This book is aimed at research workers using statistical methods as well as statisticians involved in empirical research.

In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.

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, that is, between action and outcome. In cognitive terms, the question becomes one of how 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. These 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 question is not merely about probability (or logic), but about action. The framework offers a new understanding of mind: thought is about the effects of intervention, so cognition is thereby intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. In this book, Steven Sloman offers a conceptual introduction to the key mathematical ideas in the framework, presenting them in a non-technical way, by focusing on the intuitions rather than the theorems. He tries to show why the ideas are important to understanding how people explain things, and why it is so central to human action to think not only about the world as it is, but also about the world as it could be. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning. In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms - in terms of action and manipulation.

This second edition of David-Hillel Ruben's influential and highly acclaimed book on the philosophy of explanation has been revised and expanded, and the author has made substantial changes in light of the extensive reviews the first edition received. Ruben's views on the place of laws in explanation has been refined and clarified. What is perhaps the central thesis of the book, his realist view of explanation, describing the way in which explanation depends on metaphysics, has been updated and extended and engages with some of the work in this area published since the book's first edition.

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation
Analysis introduces latent variable models by utilizing path diagrams to explain the
relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models.
The informal writing style and the numerous illustrative examples make the book
accessible to readers of varying backgrounds. Notes at the end of each chapter
expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapter’s examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the book’s examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R.
An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.

In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that readers apply the theory immediately. Introduction to Econometrics, Brief, is a streamlined version of their text, including the fundamental topics, an early review of statistics and probability, the core material of regression with cross-sectional data, and a capstone chapter on conducting empirical analysis. Introduction and Review: Economic Questions and Data; Review of Probability; Review of Statistics. Fundamentals of Regression Analysis: Linear Regression with One Regressor; Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals in the Single-Regressor Model; Linear Regression with Multiple Regressors; Hypothesis Tests and Confidence Intervals in the Multiple Regressor Model; Nonlinear Regression Functions; Assessing Studies Based on Multiple Regression; Conducting a Regression Study Using Economic Data. MARKET : For all readers interested in econometrics.