Statistics and Causality

Methods for Applied Empirical Research
 
 
John Wiley & Sons Inc (Verlag)
  • erschienen am 20. Mai 2016
  • |
  • 480 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-94705-0 (ISBN)
 
A one-of-a-kind guide to identifying and dealing with modern statistical developments in causality
Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses.
The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes:
* New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories
* End-of-chapter bibliographies that provide references for further discussions and additional research topics
* Discussions on the use and applicability of software when appropriate
Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.
weitere Ausgaben werden ermittelt
Wolfgang Wiedermann, PhD, is Assistant Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri, Columbia. His research interests include the development of methods for direction dependence analysis and causal inference, the development and evaluation of methods for person-oriented research, and methods for intensive longitudinal data.
Alexander von Eye, PhD, is Professor Emeritus of Psychology at Michigan State University. His research interests include statistical methods, categorical data analysis, and human development. Dr. von Eye is Section Editor for the Encyclopedia of Statistics in Behavioral Science and is the coauthor of Log-Linear Modeling: Concepts, Interpretation, and Application, both published by Wiley.
  • Cover
  • Title Page
  • Copyright
  • Contents
  • List of Contributors
  • Preface
  • Acknowledgments
  • Part I Bases of Causality
  • Chapter 1 Causation and the Aims of Inquiry
  • 1.1 Introduction
  • 1.2 The Aim of an Account of Causation
  • 1.2.1 The Possible Utility of a False Account
  • 1.2.2 Inquiry's Aim
  • 1.2.3 Role of "Intuitions"
  • 1.3 The Good News
  • 1.3.1 The Core Idea
  • 1.3.2 Taxonomizing "Conditions"
  • 1.3.3 Unpacking "Dependence"
  • 1.3.4 The Good News, Amplified
  • 1.4 The Challenging News
  • 1.4.1 Multiple Realizability
  • 1.4.2 Protracted Causes
  • 1.4.3 Higher Level Taxonomies and "Normal" Conditions
  • 1.5 The Perplexing News
  • 1.5.1 The Centrality of "Causal Process"
  • 1.5.2 A Speculative Proposal
  • Chapter 2 Evidence and Epistemic Causality
  • 2.1 Causality and Evidence
  • 2.2 The Epistemic Theory of Causality
  • 2.3 The Nature of Evidence
  • 2.4 Conclusion
  • Part II Directionality of Effects
  • Chapter 3 Statistical Inference for Direction of Dependence in Linear Models
  • 3.1 Introduction
  • 3.2 Choosing the Direction of a Regression Line
  • 3.3 Significance Testing for the Direction of a Regression Line
  • 3.4 Lurking Variables and Causality
  • 3.4.1 Two Independent Predictors
  • 3.4.2 Confounding Variable
  • 3.4.3 Selection of a Subpopulation
  • 3.5 Brain and Body Data Revisited
  • 3.6 Conclusions
  • Chapter 4 Directionality of Effects in Causal Mediation Analysis
  • 4.1 Introduction
  • 4.2 Elements of Causal Mediation Analysis
  • 4.3 Directionality of Effects in Mediation Models
  • 4.4 Testing Directionality Using Independence Properties of Competing Mediation Models
  • 4.4.1 Independence Properties of Bivariate Relations
  • 4.4.2 Independence Properties of the Multiple Variable Model
  • 4.4.3 Measuring and Testing Independence
  • 4.5 Simulating the Performance of Directionality Tests
  • 4.5.1 Results
  • 4.6 Empirical Data Example: Development of Numerical Cognition
  • 4.7 Discussion
  • Chapter 5 Direction of Effects in Categorical Variables: A Structural Perspective
  • 5.1 Introduction
  • 5.2 Concepts of Independence in Categorical Data Analysis
  • 5.3 Direction Dependence in Bivariate Settings: Metric and Categorical Variables
  • 5.3.1 Simulating the Performance of Nonhierarchical Log-Linear Models
  • 5.4 Explaining the Structure of Cross-Classifications
  • 5.5 Data Example
  • 5.6 Discussion
  • Chapter 6 Directional Dependence Analysis Using Skew-Normal Copula-Based Regression
  • 6.1 Introduction
  • 6.2 Copula-Based Regression
  • 6.2.1 Copula
  • 6.2.2 Copula-Based Regression
  • 6.3 Directional Dependence in the Copula-Based Regression
  • 6.4 Skew-Normal Copula
  • 6.5 Inference of Directional Dependence Using Skew-Normal Copula-Based Regression
  • 6.5.1 Estimation of Copula-Based Regression
  • 6.5.2 Detection of Directional Dependence and Computation of the Directional Dependence Measures
  • 6.6 Application
  • 6.7 Conclusion
  • Chapter 7 Non-Gaussian Structural Equation Models for Causal Discovery
  • 7.1 Introduction
  • 7.2 Independent Component Analysis
  • 7.2.1 Model
  • 7.2.2 Identifiability
  • 7.2.3 Estimation
  • 7.3 Basic Linear Non-Gaussian Acyclic Model
  • 7.3.1 Model
  • 7.3.2 Identifiability
  • 7.3.3 Estimation
  • 7.4 LINGAM for Time Series
  • 7.4.1 Model
  • 7.4.2 Identifiability
  • 7.4.3 Estimation
  • 7.5 LINGAM with Latent Common Causes
  • 7.5.1 Model
  • 7.5.2 Identifiability
  • 7.5.3 Estimation
  • 7.6 Conclusion and Future Directions
  • Chapter 8 Nonlinear Functional Causal Models for Distinguishing Cause from Effect
  • 8.1 Introduction
  • 8.2 Nonlinear Additive Noise Model
  • 8.2.1 Definition of Model
  • 8.2.2 Likelihood Ratio for Nonlinear Additive Models
  • 8.2.3 Information-Theoretic Interpretation
  • 8.2.4 Likelihood Ratio and Independence-Based Methods
  • 8.3 Post-Nonlinear Causal Model
  • 8.3.1 The Model
  • 8.3.2 Identifiability of Causal Direction
  • 8.3.3 Determination of Causal Direction Based on the PNL Causal Model
  • 8.4 On the Relationships Between Different Principles for Model Estimation
  • 8.5 Remark on General Nonlinear Causal Models
  • 8.6 Some Empirical Results
  • 8.7 Discussion and Conclusion
  • Part III Granger Causality and Longitudinal Data Modeling
  • Chapter 9 Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity
  • 9.1 Introduction
  • 9.2 Some Initial Remarks on the Logic of Granger Causality Testing
  • 9.3 Preliminary Introduction to Time Series Analysis
  • 9.4 Overview of Granger Causality Testing in the Time Domain
  • 9.5 Granger Causality Testing in the Frequency Domain
  • 9.5.1 Two Equivalent Representations of a VAR(a)
  • 9.5.2 Partial Directed Coherence (PDC) as a Frequency-Domain Index of Granger Causality
  • 9.5.3 Some Preliminary Comments
  • 9.5.4 Application to Simulated Data
  • 9.6 A New Data-Driven Solution to Granger Causality Testing
  • 9.6.1 Fitting a uSEM
  • 9.6.2 Extending the Fit of a uSEM
  • 9.6.3 Application of the Hybrid VAR Fit to Simulated Data
  • 9.7 Extensions to Nonstationary Series and Heterogeneous Replications
  • 9.7.1 Heterogeneous Replications
  • 9.7.2 Nonstationary Series
  • 9.8 Discussion and Conclusion
  • Chapter 10 Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models
  • 10.1 Introduction
  • 10.2 Granger Causation
  • 10.3 The Rasch Model
  • 10.4 Longitudinal Item Response Theory Models
  • 10.5 Data Example: Scientific Literacy in Preschool Children
  • 10.6 Discussion
  • Chapter 11 Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences
  • 11.1 Introduction
  • 11.1.1 Causality Problems in Life Sciences
  • 11.1.2 Outline of the Chapter
  • 11.1.3 Notation
  • 11.2 Granger Causality and Multivariate Granger Causality
  • 11.2.1 Granger Causality
  • 11.2.2 Multivariate Granger Causality
  • 11.3 Gene Regulatory Networks
  • 11.4 Regularization of Ill-Posed Inverse Problems
  • 11.5 Multivariate Granger Causality Approaches Using l1 and l2 Penalties
  • 11.6 Applied Quality Measures
  • 11.7 Novel Regularization Techniques with a Case Study of Gene Regulatory Networks Reconstruction
  • 11.7.1 Optimal Graphical Lasso Granger Estimator
  • 11.7.2 Thresholding Strategy
  • 11.7.3 An Automatic Realization of the GLG-Method
  • 11.7.4 Granger Causality with Multi-Penalty Regularization
  • 11.7.5 Case Study of Gene Regulatory Network Reconstruction
  • 11.8 Conclusion
  • Chapter 12 Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models
  • 12.1 Introduction
  • 12.2 Types of Reciprocal Relationship Models
  • 12.2.1 Cross-Lagged Panel Approaches
  • 12.2.2 Granger Causality
  • 12.2.3 Epistemic Causality
  • 12.2.4 Reciprocal Causality
  • 12.3 Unmeasured Reciprocal and Autocausal Effects
  • 12.3.1 Bias in Standardized Regression Weight
  • 12.3.2 Autocausal Effects
  • 12.3.3 Instrumental Variables
  • 12.4 Longitudinal Data Settings
  • 12.4.1 Monte Carlo Simulation
  • 12.4.2 Real-World Data Examples
  • 12.5 Discussion
  • Part IV Counterfactual Approaches and Propensity Score Analysis
  • Chapter 13 Log-Linear Causal Analysis of Cross-Classified Categorical Data
  • 13.1 Introduction
  • 13.2 Propensity Score Methods and the Collapsibility Problem for the Logit Model
  • 13.3 Theorem On Standardization and the Lack of Collapsibility of the Logit Model
  • 13.4 The Problem of Zero-Sample Estimates of Conditional Probabilities and the Use of Semiparametric Models to Solve the Problem
  • 13.4.1 The Problem of Zero-Sample Estimates of Conditional Probabilities
  • 13.4.2 Method for Obtaining Adjusted Two-Way Frequency Data for the Analysis of Association between X and Y
  • 13.4.3 Method for Obtaining an Adjusted Three-Way Frequency Table for the Analysis of Conditional Association
  • 13.5 Estimation of Standard Errors in the Analysis of Association with Adjusted Contingency Table Data
  • 13.6 Illustrative Application
  • 13.6.1 Data
  • 13.6.2 Software
  • 13.6.3 Analysis
  • 13.7 Conclusion
  • Chapter 14 Design- and Model-Based Analysis of Propensity Score Designs
  • 14.1 Introduction
  • 14.2 Causal Models and Causal Estimands
  • 14.3 Design- and Model-Based Inference with Randomized Experiments
  • 14.3.1 Design-Based Formulation
  • 14.3.2 Model-Based Formulation
  • 14.4 Design- and Model-Based Inferences with PS Designs
  • 14.4.1 Propensity Score Designs
  • 14.4.2 Design- versus Model-Based Formulations of PS Designs
  • 14.4.3 Other Propensity Score Techniques
  • 14.5 Statistical Issues with PS Designs in Practice
  • 14.5.1 Choice of a Specific PS Design
  • 14.5.2 Estimation of Propensity Scores
  • 14.5.3 Estimating and Testing the Treatment Effect
  • 14.6 Discussion
  • Chapter 15 Adjustment when Covariates are Fallible
  • 15.1 Introduction
  • 15.2 Theoretical Framework
  • 15.2.1 Definition of Causal Effects
  • 15.2.2 Identification of Causal Effects
  • 15.2.3 Adjusting for Latent or Fallible Covariates
  • 15.3 The Impact of Measurement Error in Covariates on Causal Effect Estimation
  • 15.3.1 Theoretical Impact of One Fallible Covariate
  • 15.3.2 Investigation of the Impact of Fallible Covariates in Simulation Studies
  • 15.3.3 Investigation of the Impact of Fallible Covariates in an Empirical Study
  • 15.4 Approaches Accounting for Latent Covariates
  • 15.4.1 Latent Covariates in Propensity Score Methods
  • 15.4.2 Latent Covariates in ANCOVA Models
  • 15.4.3 Performance of the Approaches in an Empirical Study
  • 15.5 The Impact of Additional Covariates on the Biasing Effect of a Fallible Covariate
  • 15.5.1 Investigation of the Impact of Additional Covariates in an Empirical Study
  • 15.5.2 Investigation of the Impact of Additional Covariates in Simulation Studies
  • 15.6 Discussion
  • Chapter 16 Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile
  • 16.1 Introduction
  • 16.2 Latent Class Analysis
  • 16.2.1 LCA With Covariates
  • 16.3 Propensity Score Analysis
  • 16.3.1 Inverse Propensity Weights (IPWs)
  • 16.4 Empirical Demonstration
  • 16.4.1 The Causal Question: A Moderated Average Causal Effect
  • 16.4.2 Participants
  • 16.4.3 Measures
  • 16.4.4 Analytic Strategy for LCA With Causal Inference
  • 16.4.5 Results From Empirical Demonstration
  • 16.5 Discussion
  • 16.5.1 Limitations
  • Part V Designs for Causal Inference
  • Chapter 17 Can We Establish Causality with Statistical Analyses? The Example of Epidemiology
  • 17.1 Why a Chapter on Design?
  • 17.2 The Epidemiological Theory of Causality
  • 17.3 Cohort and Case-Control Studies
  • 17.4 Improving Control in Epidemiological Research
  • 17.4.1 Measurement
  • 17.4.2 Mendelian Randomization
  • 17.4.3 Surrogate Endpoints (Experimental)
  • 17.4.4 Other Design Measures to Increase Control
  • 17.4.5 Methods of Analysis
  • 17.5 Conclusion: Control in Epidemiological Research Can Be Improved
  • Index
  • EULA

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