Skip to Main Content

Advertisement

MIT Press Direct, home
header search
Search
Advanced Search
Skip Nav Destination

Structural Intervention Distance for Evaluating Causal Graphs

Neural Computation (2015) 27 (3): 771–799.
This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.
Ensemble framework for causality learning with heterogeneous Directed Acyclic Graphs through the lens of optimization
  • Babak Aslani
  • Shima Mohebbi
Computers & Operations Research (2023) 152: 106148.
Incorporating Normalized L1 Penalty and Eigenvalue Constraint for Causal Structure Learning
  • Yunfeng Wang
  • Yuelong Zhu
  • Tingting Hang
  • Jiamin Lu
  • Jun Feng
International Journal on Artificial Intelligence Tools (2023) 32 (05)
Kernel-Based Independence Tests for Causal Structure Learning on Functional Data
  • Felix Laumann
  • Julius von Kügelgen
  • Junhyung Park
  • Bernhard Schölkopf
  • Mauricio Barahona
Entropy (2023) 25 (12): 1597.
A survey of Bayesian Network structure learning
  • Neville Kenneth Kitson
  • Anthony C. Constantinou
  • Zhigao Guo
  • Yang Liu
  • Kiattikun Chobtham
Artificial Intelligence Review (2023) 56 (8): 8721.
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
  • Matthew J. Vowels
  • Necati Cihan Camgoz
  • Richard Bowden
ACM Computing Surveys (2023) 55 (4): 1.
Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks
  • Xiaohan Wei
  • Yulai Zhang
  • Cheng Wang
Entropy (2022) 24 (10): 1351.
Evaluation Methods and Measures for Causal Learning Algorithms
  • Lu Cheng
  • Ruocheng Guo
  • Raha Moraffah
  • Paras Sheth
  • K. Selcuk Candan
  • Huan Liu
IEEE Transactions on Artificial Intelligence (2022) 3 (6): 924.
Why does the president tweet this? Discovering reasons and contexts for politicians’ tweets from news articles
  • Ziyue Li
  • Hang Hu
  • He Wang
  • Luwei Cai
  • Haipeng Zhang
  • Kunpeng Zhang
Information Processing & Management (2022) 59 (3): 102892.
A Survey on Causal Discovery: Theory and Practice
  • Alessio Zanga
  • Elif Ozkirimli
  • Fabio Stella
International Journal of Approximate Reasoning (2022) 151: 101.
BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes
  • Yury Kaminsky
  • Irina Deeva
Procedia Computer Science (2022) 212: 191.
Causal discovery in machine learning: Theories and applications
  • Ana Rita Nogueira
  • João Gama
  • Carlos Abreu Ferreira
Journal of Dynamics & Games (2021) 8 (3): 203.
Automated hyperparameter selection for the PC algorithm
  • Eric V. Strobl
Pattern Recognition Letters (2021) 151: 288.
Structural learning and estimation of joint causal effects among network-dependent variables
  • Federico Castelletti
  • Alessandro Mascaro
Statistical Methods & Applications (2021) 30 (5): 1289.
Causal discovery in heavy-tailed models
  • Nicola Gnecco
  • Nicolai Meinshausen
  • Jonas Peters
  • Sebastian Engelke
The Annals of Statistics (2021) 49 (3)
On scoring Maximal Ancestral Graphs with the Max–Min Hill Climbing algorithm
  • Konstantinos Tsirlis
  • Vincenzo Lagani
  • Sofia Triantafillou
  • Ioannis Tsamardinos
International Journal of Approximate Reasoning (2018) 102: 74.
Partition MCMC for Inference on Acyclic Digraphs
  • Jack Kuipers
  • Giusi Moffa
Journal of the American Statistical Association (2017) 112 (517): 282.

Advertisement

  • Online ISSN 1530-888X
  • Print ISSN 0899-7667
A product of The MIT Press
Newsletter Sign Up

MIT Press Direct

Information

MIT Press

Contact Us

© 2023 The MIT Press
Close Modal
This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. No content on this site may be used to train artificial intelligence systems without permission in writing from the MIT Press.
Accept