000			05678cam a2200433 i 4500
001			38700
003			0000000000
005			20240411192905.0
008			151217t20162016nju s 000 0 eng c
010			_a 2015047424
020			_a1118947045 (cloth)
020			_a9781118947043 (cloth)
035			_a18912704
040			_aWaSeSS/DLC _beng _cWaSeSS _erda _dDLC
042			_apcc
050	0	0	_aQA276.A2 _bS73 2016
082	0	0	_a001.4/22 _223
245	0	0	_aStatistics and causality : _bmethods for applied empirical research / _cedited by Wolfgang Wiedermann, Alexander von Eye.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, _c[2016]
264		4	_c©2016
300			_a1 online resource.
336			_atext _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
490	1		_aWiley series in probability and statistics
500			_aIncludes bibliographical references and index.
504			_aIncludes bibliographical references and index.
505	0		_aBASES OF CAUSALITY. Causation and the Aims of Inquiry / Ned Hall -- Evidence and Epistemic Causality / Michael Wilde, Jon Williamson -- DIRECTIONALITY OF EFFECTS. Statistical Inference for Direction of Dependence in Linear Models / Yadolah Dodge, Valentin Rousson -- Directionality of Effects in Causal Mediation Analysis / Wolfgang Wiedermann, Alexander Eye -- Direction of Effects in Categorical Variables: A Structural Perspective / Alexander Eye, Wolfgang Wiedermann -- Directional Dependence Analysis Using Skew-Normal Copula-Based Regression / Seongyong Kim, Daeyoung Kim -- Non-Gaussian Structural Equation Models for Causal Discovery / Shohei Shimizu -- Nonlinear Functional Causal Models for Distinguishing Cause from Effect / Kun Zhang, Aapo Hyvarinen -- GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING. Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity / Peter C M Molenaar, Lawrence L Lo -- Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models / Ingrid Koller, Claus H Carstensen, Wolfgang Wiedermann, Alexander von Eye -- Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences / Katerina Hlavkov-Schindler, Valeriya Naumova, Sergiy Pereverzyev -- Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models / Phillip K Wood -- COUNTERFACTUAL APPROACHES AND PROPENSITY SCORE ANALYSIS. Log-Linear Causal Analysis of Cross-Classified Categorical Data / Kazuo Yamaguchi -- Design- and Model-Based Analysis of Propensity Score Designs / Peter M Steiner -- Adjustment when Covariates are Fallible / Steffi Pohl, Marie-Ann Sengewald, Rolf Steyer -- Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile / Stephanie T Lanza, Megan S Schuler, Bethany C Bray -- DESIGNS FOR CAUSAL INFERENCE. Can We Establish Causality with Statistical Analyses? The Example of Epidemiology / Ulrich Frick, Jurgen Rehm.
520			_aA 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.
650		0	_aCausation.
650		0	_aQuantitative research _xMethodology.
650		0	_aStatistics _xMethodology.
700	1		_aEye, Alexander von, _eeditor.
700	1		_aWiedermann, Wolfgang, _d1981- _eeditor.
776	0	8	_iOnline version: _tStatistics and causality _dHoboken, New Jersey : John Wiley & Sons, 2016 _z9781118947050 _w(DLC) 2015050865
830		0	_aWiley series in probability and statistics.
856			_uhttps://drive.google.com/file/d/1JjY1GOPIpnlYq74ezgvIOBK-jRbyuOQY/view?usp=sharing
999			_c9931 _d9931