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Emilija Perković
Emilija Perković
Assistant Professor, Department of Statistics, University of Washington
Verified email at uw.edu
Title
Cited by
Cited by
Year
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
1062018
Graphical criteria for efficient total effect estimation via adjustment in causal linear models
L Henckel, E Perković, MH Maathuis
Journal of the Royal Statistical Society: Series B 84 (2), 579-599, 2022
862022
A complete generalized adjustment criterion
E Perković, J Textor, M Kalisch, MH Maathuis
Uncertainty in Artificial Intelligence 2015, 2015
742015
Interpreting and using CPDAGs with background knowledge
E Perković, M Kalisch, MH Maathuis
Uncertainty in Artificial Intelligence 2017, 2017
522017
Identifying causal effects in maximally oriented partially directed acyclic graphs
E Perković
Uncertainty in Artificial Intelligence 2020, 2020
262020
Efficient least squares for estimating total effects under linearity and causal sufficiency
FR Guo, E Perković
Journal of Machine Learning Research 23 (104), 1-41, 2022
142022
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
142021
pcalg: Methods for graphical models and causal inference
M Kalisch, A Hauser, M Maechler, D Colombo, D Entner, P Hoyer, ...
R Package retrieved from https://CRAN. R-project. org/package= pcalg, 2021
72021
Graphical characterizations of adjustment sets
E Perković
ETH Zurich, 2018
52018
Package ‘pcalg’
M Kalisch, A Hauser, M Maechler, D Colombo, D Entner, P Hoyer, ...
42024
Variable elimination, graph reduction and the efficient g-formula
FR Guo, E Perković, A Rotnitzky
Biometrika 110 (3), 739-761, 2023
32023
Conditional Adjustment in a Markov Equivalence Class
S LaPlante, E Perković
arXiv preprint arXiv:2311.06458, 2023
2023
Leadership in Statistics and Data Science: Planning for Inclusive Excellence, by Amanda L. Golbeck, ed. Cham, Switzerland, Springer, 2021, xxvii+ 432 pp., $159.99 (H), ISBN …
E Perković
The American Statistician 76 (3), 306-307, 2022
2022
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