Two optimal strategies for active learning of causal models from interventional data

DOI10.1016/j.ijar.2013.11.007zbMath1390.68530arXiv1205.4174OpenAlexW2162690533MaRDI QIDQ2440180

Alain Hauser

Publication date: 27 March 2014

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1205.4174

zbMATH Keywords

interventionsactive learningBayesian networkcausal inferencegraphical model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (5)

Fast causal orientation learning in directed acyclic graphs ⋮ Causal structure learning: a combinatorial perspective ⋮ Sound and complete causal identification with latent variables given local background knowledge ⋮ Marginal integration for nonparametric causal inference ⋮ Unnamed Item

Uses Software

• pcalg
• TETRAD

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• On separating systems of graphs
• A characterization of Markov equivalence classes for acyclic digraphs
• Triangulated graphs and the elimination process
• Active Learning
• Nonparametric Estimation from Incomplete Observations
• Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach
• Causal diagrams for empirical research
• Algorithmic Aspects of Vertex Elimination on Graphs
• 10.1162/153244303321897717
• Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs

This page was built for publication: Two optimal strategies for active learning of causal models from interventional data