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Discovering Causal Structures in Bayesian Gaussian Directed Acyclic Graph Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Bayesian Active Learning Experimental Design for Inferring Signaling Networks
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Learning Bayesian networks: The combination of knowledge and statistical data
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Statistics and causal inference: A review
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Introduction to Structural Equation Models
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An interactive approach for Bayesian network learning using domain/expert knowledge
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Two optimal strategies for active learning of causal models from interventional data
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Correlation and Causation
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Bayesian Inference of Multiple Gaussian Graphical Models
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Analysis of Gene Sets Based on the Underlying Regulatory Network
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Identifiability of Gaussian structural equation models with equal error variances
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Sparse inverse covariance estimation with the graphical lasso
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Jointly Interventional and Observational Data: Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
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Causal Inference by using Invariant Prediction: Identification and Confidence Intervals
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Inferring Cellular Networks Using Probabilistic Graphical Models
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Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
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Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data
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Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
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Learning Markov Equivalence Classes of Directed Acyclic Graphs: An Objective Bayes Approach
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Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways
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A characterization of Markov equivalence classes for acyclic digraphs
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Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
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[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention
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Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
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Causal Discovery with Continuous Additive Noise Models
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Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
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Causal discovery with continuous additive noise models
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Causation, Prediction, and Search
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Predictive models are indeed useful for causal inference
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Bayesian Sample Size Determination for Causal Discovery
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Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states
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MCMC Structure Learning for Causal DAGs under Interval-Valued Uncertainty: A Bayesian Procedure with Asymptotic Correctness
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Improving uncertain reasoning combining probabilistic relational models and expert knowledge
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