Contents -- Preface -- 1 Preliminaries -- 1.1 The shadow's cause -- 1.2 Fisher's genius and the randomised experiment -- 1.3 The controlled experiment -- 1.4 Physical controls and observational controls -- 2 From cause to correlation and back -- 2.1 Translating from causal to statistical models -- 2.2 Directed graphs -- 2.3 Causal conditioning -- 2.4 d-separation -- 2.5 Probability distributions -- 2.6 Probabilistic independence -- 2.7 Markov condition -- 2.8 The translation from causal models to observational -- 2.9 Counterintuitive consequences and limitations of d-separation: conditioning on a causal child -- 2.10 Counterintuitive consequences and limitations of d-separation: conditioning due to selection bias -- 2.11 Counterintuitive consequences and limitations of d-separation: feedback loops and cyclic causal graphs -- 2.12 Counterintuitive consequences and limitations of d-separation: imposed conservation relationships -- 2.13 Counterintuitive consequences and limitations of d-separation: unfaithfulness -- 2.14 Counterintuitive consequences and limitations of d-separation: context-sensitive independence -- 2.15 The logic of causal inference -- 2.16 Statistical control is not always the same as physical control -- 2.17 A taste of things to come -- 3 Sewall Wright, path analysis and d-separation -- 3.1 A bit of history -- 3.2 Why Wright's method of path analysis was ignored -- 3.3 d-sep tests -- 3.4 Independence of d-separation statements -- 3.5 Testing for probabilistic independence -- 3.6 Permutation tests of independence -- 3.7 Form-free regression -- 3.8 Conditional independence -- 3.9 Spearman partial correlations -- 3.10 Seed production in St Lucie's Cherry -- 3.11 Specific leaf area and leaf gas exchange -- 4 Path analysis and maximum likelihood -- 4.1 Testing path models using maximum likelihood.

4.2 Decomposing effects in path diagrams -- 4.3 Multiple regression expressed as a path model -- 4.4 Maximum likelihood estimation of the gas-exchange model -- 5 Measurement error and latent variables -- 5.1 Measurement error and the inferential tests -- 5.2 Measurement error and the estimation of path coefficients -- 5.3 A measurement model -- 5.4 The nature of latent variables -- 5.5 Horn dimensions in Bighorn Sheep -- 5.6 Body size in Bighorn Sheep -- 5.7 Name calling -- 6 The structural equations model -- 6.1 Parameter identification -- 6.2 Structural underidentification with measurement models -- 6.3 Structural underidentification with structural models -- 6.4 Behaviour of the maximum likelihood chi-squared statistic with small sample size -- 6.5 Behaviour of the maximum likelihood chi-squared statistic with data that do not follow a multivariate normal distribution -- 6.6 Solutions for modelling non-normally distributed variables -- 6.7 Alternative measures of 'approximate' fit -- 6.8 Bentler's comparative fit index -- 6.9 Approximate fit measured by the root mean square error of approximation -- 6.10 An SEM analysis of the Bumpus House Sparrow data -- 7 Nested models and multilevel models -- 7.1 Nested models -- 7.2 Multigroup models -- 7.3 The dangers of hierarchically structured data -- 7.4 Multilevel SEM -- 8 Exploration, discovery and equivalence -- 8.1 Hypothesis generation -- 8.2 Exploring hypothesis space -- 8.3 The shadow's cause revisited -- 8.4 Obtaining the undirected dependency graph -- 8.5 The undirected dependency graph algorithm8 -- 8.6 Interpreting the undirected dependency graph -- 8.7 Orienting edges in the undirected dependency graph using unshielded colliders assuming an acyclic causal structure -- 8.8 Orientation algorithm using unshielded colliders.

8.9 Orienting edges in the undirected dependency graph using definite discriminating paths -- 8.10 The Causal Inference algorithm -- 8.11 Equivalent models -- 8.12 Detecting latent variables -- 8.13 Vanishing Tetrad algorithm -- 8.14 Separating the message from the noise -- 8.15 The Causal Inference algorithm and sampling error -- 8.16 The Vanishing Tetrad algorithm and sampling variation -- 8.17 Empirical examples -- 8.18 Orienting edges in the undirected dependency graph without assuming an acyclic causal structure -- 8.19 The Cyclic Causal Discovery algorithm -- 8.20 In conclusion . . . -- Appendix -- References -- Index.