
Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding
We present a method for assessing the sensitivity of the true causal eff...
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On the NonMonotonicity of a NonDifferentially Mismeasured Binary Confounder
Suppose that we are interested in the average causal effect of a binary ...
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Towards Conditional Path Analysis
We extend path analysis by giving sufficient conditions for computing th...
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On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder
Suppose that we are interested in the average causal effect of a binary ...
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Conditional Path Analysis in SinglyConnected Path Diagrams
We extend the classical path analysis by showing that, for a singlyconn...
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Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
An intervention may have an effect on units other than those to which th...
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Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
In this paper, we prove that some Gaussian structural equation models wi...
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Identification of Strong Edges in AMP Chain Graphs
The essential graph is a distinguished member of a Markov equivalence cl...
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Unifying DAGs and UGs
We introduce a new class of graphical models that generalizes Lauritzen...
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Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models
We introduce a new family of graphical models that consists of graphs wi...
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Representing Independence Models with Elementary Triplets
In an independence model, the triplets that represent conditional indepe...
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Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
We extend AnderssonMadiganPerlman chain graphs by (i) relaxing the sem...
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Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs
We address some computational issues that may hinder the use of AMP chai...
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Every LWF and AMP chain graph originates from a set of causal models
This paper aims at justifying LWF and AMP chain graphs by showing that t...
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Error AMP Chain Graphs
Any regular Gaussian probability distribution that can be represented by...
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Marginal AMP Chain Graphs
We present a new family of models that is based on graphs that may have ...
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Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version
This paper deals with chain graphs under the AnderssonMadiganPerlman (...
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Approximate Counting of Graphical Models Via MCMC Revisited
In Peña (2007), MCMC sampling is applied to approximately calculate the ...
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Combinatorial Optimization by Learning and Simulation of Bayesian Networks
This paper shows how the Bayesian network paradigm can be used in order ...
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On Local Optima in Learning Bayesian Networks
This paper proposes and evaluates the kgreedy equivalence search algori...
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Identifying the Relevant Nodes Without Learning the Model
We propose a method to identify all the nodes that are relevant to compu...
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Reading Dependencies from PolytreeLike Bayesian Networks
We present a graphical criterion for reading dependencies from the minim...
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Learning AMP Chain Graphs under Faithfulness
This paper deals with chain graphs under the alternative AnderssonMadig...
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Towards Optimal Learning of Chain Graphs
In this paper, we extend Meek's conjecture (Meek 1997) from directed and...
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Finding Consensus Bayesian Network Structures
Suppose that multiple experts (or learning algorithms) provide us with a...
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Reading Dependencies from Covariance Graphs
The covariance graph (aka bidirected graph) of a probability distributi...
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Faithfulness in Chain Graphs: The Gaussian Case
This paper deals with chain graphs under the classic LauritzenWermuthF...
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