Bayesian sample size determination for causal discovery
type: Item
instance of: scholarly article
title: Bayesian sample size determination for causal discovery
author: Federico Castelletti
author: Guido Consonni
published in: Statistical Science
publication date: 2024-07-24T00:00:00Z
Mathematics Subject Classification ID: 62-XX
zbMATH DE Number: 7886120
zbMATH Keywords: active learning
zbMATH Keywords: Bayes factor
zbMATH Keywords: Bayesian experimental design
zbMATH Keywords: directed acyclic graph
zbMATH Keywords: intervention
MaRDI profile type: Publication
cites work: A characterization of Markov equivalence classes for acyclic digraphs
cites work: Alternative Markov properties for chain graphs
cites work: Philosophy of statistics.
cites work: Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways
cites work: Bayesian inference of causal effects from observational data in Gaussian graphical models
cites work: Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach
cites work: Equivalence class selection of categorical graphical models
cites work: Bayesian experimental design: A review
cites work: 10.1162/153244302760200696
cites work: Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models
cites work: Compatibility of prior specifications across linear models
cites work: Probabilistic Networks and Expert Systems
cites work: Design and analysis of experiments
cites work: Hyper Markov laws in the statistical analysis of decomposable graphical models
cites work: Statistical evidence and sample size determination for Bayesian hypothesis testing
cites work: Optimal Experimental Design for Another's Analysis
cites work: Robust Causal Structure Learning with Some Hidden Variables
cites work: Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
cites work: Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
cites work: Two optimal strategies for active learning of causal models from interventional data
cites work: Jointly Interventional and Observational Data: Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
cites work: Q3096205
cites work: Q2933927
cites work: Q5726065
cites work: On the use of Non-Local Prior Densities in Bayesian Hypothesis Tests
cites work: Q3174091
cites work: Bayes Factors
cites work: Q3655273
cites work: Q4382293
cites work: Q5658935
cites work: Estimating high-dimensional intervention effects from observational data
cites work: Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach
cites work: Q3219581
cites work: Bayesian Networks in R
cites work: Q4322324
cites work: Q4496846
cites work: Statistics and causal inference: A review. (With discussion)
cites work: Reconstruction of a directed acyclic graph with intervention
cites work: Identifiability of Gaussian structural equation models with equal error variances
cites work: Q3668654
cites work: On the Probability of Observing Misleading Statistical Evidence
cites work: A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models
DOI: 10.1214/23-STS905
description: scientific article; zbMATH DE number 7886120