Time and sample efficient discovery of Markov blankets and direct causal relations

By Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov. Knowledge Discovery and Data Mining, 2003

406 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e55939d171d1671b4cc25a48a722c9781a60cbc7

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Citations in the corpus

Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “Several empirical studies have verified their applicability (Tsamardinos et al., 2003b; Spirtes et al., 2000; Glymour and Cooper, 1999; Aliferis and Cooper, 1994).”
Algorithms for Discovery of Multiple Markov Boundaries (2013, methodology): “Finally, since very fast single Markov boundary induction algorithms have been recently introduced (Aliferis et al., 2010a, 2003a; Peña et al., 2007; Tsamardinos et al., 2003a,b), the overall TIE∗ operation is very fast.”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “, simulated annealing, max-min parent and children algorithm [348], Markov blanket algorithm [349], Markov boundary induction algorithm [350].”
DAG-GNN: DAG Structure Learning with Graph Neural Networks (2019, methodology): “Recently, there appears a suite of hybrid algorithms that combine score-based and constraint-based methods, such as MMHC (Tsamardinos et al., 2003), and apply constraint-based methods to multiple environments (Mooij et al., 2016).”
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis (2004, positive): “Furthermore, we are interested in applying various multivariate Markov blanket and local neighborhood algorithms that have been previously successfully applied to cancer gene expression and several other domains and do guarantee efficient identification of a set of relevant attributes under fairly b”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, methodology): “Additional evidence of the scalability of MMHC is found in a previously reported experiment in Tsamardinos et al. (2003a).”
Feature deduction and ensemble design of intrusion detection systems (2005, background): “Markov blanket (MB) of the output variable T, is a novel idea for significant feature selection in large data sets (Tsamardinos et al., 2003).”
Towards Non-I.I.D. image classification: A dataset and baselines (2019, methodology): “Popular methods based one observational data to estimate the causal effect of a treatment on the outcome include propensity score matching [25, 26], markov blankets [27, 28] and confounder balancing [29, 30] and etc [31].”
A survey of Bayesian Network structure learning (2021, methodology): “The Incremental Association Markov Blanket (IAMB) algorithm optimises Markov Blanket discovery so that it can handle thousands of nodes (Tsamardinos et al., 2003).”
GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data (2005, methodology): “…colleagues , we added new functionality to the system, namely, several simple gene expression normalization methods, area under ROC curve performance metric (for binary diagnostic problems), and two state of the art local causal discovery algorithms [17] [18] shown with boldface in Table 4.”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, methodology): “…Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) (Margolin et al., 2006), the Max-Min Parent and Children algorithm (MMHC) (Tsamardinos et al., 2003), the Markov blanket algorithm (HITON-PC) (Aliferis et al., 2010), the GENIE3 algorithm (Irrthum et al., 2010) and the ANOVA…”
Learning Bayesian networks: approaches and issues (2011, methodology): “Aliferis and Tsamardinos (2002) and Tsamardinos et al. (2003a, 2003b) focus on learning local features of a Bayesian network.”