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Using Path Diagrams as a Structural Equation Modelling Tool
, 1997
"... this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include: ..."
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Cited by 32 (7 self)
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this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include:
A Polynomial Time Algorithm For Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias
, 1997
"... ations. For this class of algorithms, it is impossible to determine which of two dseparation equivalent causal structures generated a given probability distribution, given only the set of conditional independence and dependence relations true of the observed distribution. We will describe a polynom ..."
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Cited by 16 (5 self)
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ations. For this class of algorithms, it is impossible to determine which of two dseparation equivalent causal structures generated a given probability distribution, given only the set of conditional independence and dependence relations true of the observed distribution. We will describe a polynomial (in the number of vertices) time algorithm for determining when two DAGs which may have latent variables or selection bias are dseparation equivalent. A DAG G entails a conditional independence relation if and only if it is true in every probability measure satisfying the local directed Markov property for G. (We place definitions and sets of variables in boldface.) Pearl, Geiger, and Verma (Pearl 1988) have shown that there is a graphical relation, dseparation, that holds among three disjoint sets of variable A, and B, and C in DAG G if and only if G entails that
The TETRAD Project: Constraint Based Aids to Causal Model Specification
 MULTIVARIATE BEHAVIORAL RESEARCH
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Discovering Cyclic Causal Structure
, 1996
"... This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no ..."
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Cited by 2 (1 self)
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This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no unmeasured common causes (latent variables). Linear causal structures of this type can be represented by nonrecursive linear structural equation models. I present a correct polynomial time (on sparse graphs) discovery algorithm for linear cyclic models that contain no latent variables. This algorithm outputs a representation of a class of nonrecursive linear structural equation models, given observational data as input. Under the assumption that all conditional independencies found in the observational data are true for structural reasons rather than because of particular parameter values, the algorithm discovers causal features of the structure which generated the data. A simple modification of the algorithm can be used)as a decision procedure (whose runtime is polynomial in the number of vertices) for determining when two
1Using Path Diagrams as a Structural Equation Modelling Tool
"... Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error ” ..."
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Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error ” or “disturbance ” terms), and an associated path