Emilija Perković
Emilija Perković
Assistant Professor, Department of Statistics, University of Washington
Verified email at uw.edu
Cited by
Cited by
A complete generalized adjustment criterion
E Perković, J Textor, M Kalisch, MH Maathuis
Uncertainty in Artificial Intelligence 2015, 2015
Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs
E Perkovic, J Textor, M Kalisch, MH Maathuis
The Journal of Machine Learning Research 18 (1), 8132-8193, 2018
Interpreting and using CPDAGs with background knowledge
E Perković, M Kalisch, MH Maathuis
Uncertainty in Artificial Intelligence 2017, 2017
Graphical criteria for efficient total effect estimation via adjustment in causal linear models
L Henckel, E Perković, MH Maathuis
arXiv preprint arXiv:1907.02435, 2019
Identifying causal effects in maximally oriented partially directed acyclic graphs
E Perković
Uncertainty in Artificial Intelligence 2020, 2020
Efficient least squares for estimating total effects under linearity and causal sufficiency
FR Guo, E Perković
arXiv preprint arXiv:2008.03481, 2020
Package ‘pcalg’
M Kalisch, A Hauser, M Maechler, D Colombo, D Entner, P Hoyer, ...
Graphical characterizations of adjustment sets
E Perković
ETH Zurich, 2018
Minimal enumeration of all possible total effects in a Markov equivalence class
R Guo, E Perkovic
International Conference on Artificial Intelligence and Statistics, 2395-2403, 2021
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