A survey on causal discovery: theory and practice

DOI10.1016/j.ijar.2022.09.004OpenAlexW4295346149MaRDI QIDQ2105567

Fabio Stella, Elif Ozkirimli, Alessio Zanga

Publication date: 8 December 2022

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

Full work available at URL: https://doi.org/10.1016/j.ijar.2022.09.004

zbMATH Keywords

causalitycausal discoverystructural learningcausal models

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Uses Software

• pcalg
• bnlearn
• TETRAD
• SynTReN

Cites Work

• Learning high-dimensional directed acyclic graphs with latent and selection variables
• CAM: causal additive models, high-dimensional order search and penalized regression
• Identifiability of nonrecursive structural equation models
• The max-min hill-climbing Bayesian network structure learning algorithm
• Modeling by shortest data description
• Estimating the dimension of a model
• Independent component analysis, a new concept?
• A characterization of Markov equivalence classes for acyclic digraphs
• Ancestral graph Markov models.
• Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes
• High-dimensional consistency in score-based and hybrid structure learning
• Foundations of structural causal models with cycles and latent variables
• Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms
• Discovering causal graphs with cycles and latent confounders: an exact branch-and-bound approach
• On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
• Causal diagrams for empirical research
• 10.1162/153244303321897717
• Consistency guarantees for greedy permutation-based causal inference algorithms
• Causal Inference Using the Algorithmic Markov Condition
• Structural Intervention Distance for Evaluating Causal Graphs
• Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
• On Pearl’s Hierarchy and the Foundations of Causal Inference
• A new look at the statistical model identification
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