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24
Graphs, Causality, And Structural Equation Models
, 1998
"... Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers. ..."
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Cited by 44 (14 self)
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Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers.
Learning the structure of linear latent variable models
 Journal of Machine Learning Research
, 2006
"... We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are dseparated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the ..."
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Cited by 41 (13 self)
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We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are dseparated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is pointwise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we
Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data
, 2000
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Causal Inference in the Presence of Latent Variables and Selection Bias
 In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence
"... This paper uses Bayesian network models for that investigation. Bayesian networks, or directed acyclic graph (DAG) models have proved very useful in representing both causal and statistical hypotheses. The nodes of the graph represent vertices, directed edges represent direct influences, and the top ..."
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Cited by 28 (4 self)
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This paper uses Bayesian network models for that investigation. Bayesian networks, or directed acyclic graph (DAG) models have proved very useful in representing both causal and statistical hypotheses. The nodes of the graph represent vertices, directed edges represent direct influences, and the topology of the graph encodes statistical constraints. We will consider features of such models that can be determined from data under assumptions that are related to those routinely applied in experimental situations:
Identifying Independencies in Causal Graphs with Feedback
 In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference
, 1996
"... We show that the dseparation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables. 1 INTRODUCTION It is well known that the dseparation test is sound and complete relative to the independencies assumed in t ..."
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Cited by 19 (0 self)
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We show that the dseparation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables. 1 INTRODUCTION It is well known that the dseparation test is sound and complete relative to the independencies assumed in the construction of Bayesian networks [Verma and Pearl, 1988, Geiger et al., 1990]. In other words, any dseparation condition in the network corresponds to a genuine independence condition in the underlying probability distribution and, conversely, every dconnection corresponds to a dependency in at least one distribution compatible with the network. The situation with feedback systems is more complicated, primarily because the probability distributions associated with such systems do not lend themselves to a simple product decomposition. The joint distribution of feedback systems cannot be written as a product of the conditional distributions of each child variable, given its parents. Rath...
Discovering Cyclic Causal Models by Independent Components Analysis
 UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 2008
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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 14 (4 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
Heuristic Greedy Search Algorithms for Latent Variable Models
, 1997
"... this paper we will describe how to extend search algorithms developed for nonlatent variable DAG models to the case of DAG models with latent variables. We will introduce two generalizations of DAGs, called mixed ancestor graphs (or MAGs) and partial ancestor graphs (or PAGs), and briefly describe ..."
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Cited by 9 (1 self)
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this paper we will describe how to extend search algorithms developed for nonlatent variable DAG models to the case of DAG models with latent variables. We will introduce two generalizations of DAGs, called mixed ancestor graphs (or MAGs) and partial ancestor graphs (or PAGs), and briefly describe how they can be used to search for latent variable DAG models, to classify, and to predict the effects of interventions in causal systems.
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
"... We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a direct ..."
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Cited by 9 (0 self)
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We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows cycles. In addition to discussing inference and sampling with this representation, we give an exponential family parametrization that allows parameter estimation to be stated as a convex optimization problem; we also give a convex relaxation of the task of simultaneous parameter and structure learning using group ℓ1regularization. The model is evaluated on simulated data and intracellular flow cytometry data. 1