Software

  • Contributing author to the R package pcalg.

Preprints

  • D. Tsao, K. Muandet, F. Eberhardt, E. Perković (2026), Lost in Aggregation: The Causal Interpretation of the IV Estimand, Submitted (arxiv)
  • S. LaPlante, S. Triantafillou, E. Perković (2026) Data-Driven Adjustment for Multiple Treatments, Sumbitted (arxiv)
  • S. LaPlante, E. Perković (2026). Identification of Conditional Causal Effects in an MPDAG. Submitted (arxiv)
  • A.Venkateswaran, E. Perković (2026). Towards Complete Causal Explanation with Expert Knowledge. Submitted (arxiv).

Publications

  • S. LaPlante, E. Perković (2024). Conditional Adjustment in a Markov Equivalence Class. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (arxiv)(published version).
  • F. R. Guo, E. Perković, A. Rotnitzky (2023). Variable elimination, graph reduction, and efficient g-formula. Biometrika.(arxiv)(published version)(R package)

  • F. R. Guo, E. Perković (2022). Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency. Journal of Machine Learning Research (arxiv)(published version)(R package)

  • L. Henckel, E. Perković and M.H. Maathuis (2022). Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models. Journal of Royal Statistical Society. Series B (arxiv)(published version)(code)(slides)

  • F. R. Guo, E. Perković (2021). Minimal enumeration of all possible total effects in a Markov equivalence class. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021).(arxiv)(published version)(R package)

  • E. Perković (2020). Identifying causal effects in maximally oriented partially directed acyclic graphs. Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI-20) (arxiv, published version: main paper, supplement) (slides: short, full)

  • E. Perković, J. Textor, M. Kalisch, and M.H. Maathuis (2018). Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs. Journal of Machine Learning Research 18 (arxiv, published version) (slides)

  • E. Perković, M. Kalisch and M.H. Maathuis (2017). Interpreting and using CPDAGs with background knowledge. In Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI-17) (arxiv, published version + supplement)(slides, poster)(simulation study code)

  • E. Perković, J. Textor, M. Kalisch and M.H. Maathuis (2015). A complete generalized adjustment criterion. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI-15). (arxiv, published version)(poster)

Theses Advised

  • Sara LaPlante, PhD Thesis (2025), Causal Effect Identification via Equivalence Classes of Acyclic Graphs and Data-Driven Adjustment, Advised by Emilija Perković, (available at UW Library, Research Collection)
  • Aparajithan Venkateswaran, PhD Thesis (2024), Problems in Identification and Estimation: Algorithms for Pathogen, Ancestral, and Rashomon Analysis co-Advised by Tyler McCormick and Emilija Perković (available at UW Library, Research Collection).
  • Emily Rose Flanagan, Honor’s Thesis (2020), Identification and Estimation of Controlled Direct Effects in DAGs, CPDAGs, and MPDAGs. (pdf)
  • Leonard Henckel, Master Thesis (2017) Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Multivariate Gaussian Distributions, Advised by Marloes Maathuis and Emilija Perković.

Thesis

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