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Commentator: A frontend userinterface module for graphical and structural equation modeling
, 2010
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Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
"... In this paper, we present an extended set of graphical criteria for the identification of direct causal effects in linear Structural Equation Models (SEMs). Previous methods of graphical identification of direct causal effects in linear SEMs include methods such as the singledoor criterion, the ins ..."
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In this paper, we present an extended set of graphical criteria for the identification of direct causal effects in linear Structural Equation Models (SEMs). Previous methods of graphical identification of direct causal effects in linear SEMs include methods such as the singledoor criterion, the instrumental variable and the IVpair, and the accessory set. However, there remain graphical models where a direct causal effect can be identified and these graphical criteria all fail. As a result, we introduce a new set of graphical criteria which uses descendants of either the cause variable or the effect variable as “pathspecific instrumental variables ” for the identification of the direct causal effect as long as certain conditions are satisfied. These conditions are based on edge removal and the existing graphical criteria of instrumental variables, and the identifiability of certain other total effects, and thus can be easily checked. 1
Parameter Identification in a Class of Linear Structural Equation Models
"... Linear causal models known as structural equation models (SEMs) are widely used for data analysis in the social sciences, economics, and artificial intelligence, in which random variables are assumed to be continuous and normally distributed. This paper deals with one fundamental problem in the appl ..."
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Linear causal models known as structural equation models (SEMs) are widely used for data analysis in the social sciences, economics, and artificial intelligence, in which random variables are assumed to be continuous and normally distributed. This paper deals with one fundamental problem in the applications of SEMs – parameter identification. The paper uses the graphical models approach and provides a procedure for solving the identification problem in a special class of SEMs. 1
Identification of Causal Effects in Linear SEMs using the Instrumental Variable Function
"... In this paper, we discuss the problem of identification of causal effects in linear Structural Equation Models (SEMs) based on causal diagrams. The two main methods for solving this problem are algebraic methods, which attempts to solve the set of simultaneous equations based on Wright’s method of p ..."
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In this paper, we discuss the problem of identification of causal effects in linear Structural Equation Models (SEMs) based on causal diagrams. The two main methods for solving this problem are algebraic methods, which attempts to solve the set of simultaneous equations based on Wright’s method of path analysis, and graphical methods, which tests for certain graphical criteria in the causal diagram of the SEM. We will introduce the Instrumental Variable Function, which we will show to correspond to both algebraic and graphical methods and is useful in understanding the identification problem.