%0 Generic %1 tsirlis2017scoring %A Tsirlis, K %A Lagani, V %A Triantafillou, S %A Tsamardinos, I %D 2017 %I 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Workshop on Causal Discovery (KDD) %K mxmcausalpath %T On Scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing Algorithm %U http://nugget.unisa.edu.au/CD2017/papersonly/maxmin-r0.pdf %X t We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max-Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraintbased first phase that greatly reduces the space of structures to investigate. We show on simulated data that the proposed algorithm greatly improves on GSMAG, and compares positively against FCI and cFCI, two well known constraint-based approaches for causal-network reconstruction in presence of latent confounders

@conference{tsirlis2017scoring, abstract = {t We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max-Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraintbased first phase that greatly reduces the space of structures to investigate. We show on simulated data that the proposed algorithm greatly improves on GSMAG, and compares positively against FCI and cFCI, two well known constraint-based approaches for causal-network reconstruction in presence of latent confounders}, added-at = {2021-03-10T10:55:47.000+0100}, author = {Tsirlis, K and Lagani, V and Triantafillou, S and Tsamardinos, I}, biburl = {https://www.bibsonomy.org/bibtex/251782ff3d0021d9ae7b7229b39a55d75/mensxmachina}, interhash = {4731b83fe8b2f1f60eed63d178912109}, intrahash = {51782ff3d0021d9ae7b7229b39a55d75}, keywords = {mxmcausalpath}, publisher = {23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Workshop on Causal Discovery (KDD)}, timestamp = {2021-03-10T10:55:47.000+0100}, title = {On Scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing Algorithm}, url = {http://nugget.unisa.edu.au/CD2017/papersonly/maxmin-r0.pdf}, year = 2017 }