Project Details
Projekt Print View

Causality: an algorithmic framework and a computational complexity perspective

Subject Area Theoretical Computer Science
Term from 2016 to 2022
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 273587939
 
Final Report Year 2021

Final Report Abstract

No abstract available

Publications

  • Efficiently Finding Conditional Instruments for Causal Inference, In Proc. of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15), AAAI Press / International Joint Conferences on Artificial Intelligence, 3243-3249, 2015
    Benito van der Zander, Johannes Textor, Maciej Liśkiewicz
  • On the Faithful DAGs of a Dependency Graph, In Proc. of the 31st Conference on Uncertainty in Artificial Intelligence (UAI’15), 882-891, 2015
    Johannes Textor, Alexander Idelberger, Maciej Liśkiewicz
  • On Searching for Generalized Instrumental Variables, In Proc. of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS’16), JMLR Proceedings, 1214-1222, 2016. A rank
    Benito van der Zander, Maciej Liśkiewicz
  • Robust causal inference using directed acyclic graphs: the R package ‘dagitty’, International journal of epidemiology 45 (6), 1887-1894 (2016)
    Johannes Textor, Benito van der Zander, Mark S Gilthorpe, Maciej Liśkiewicz, George TH Ellison
    (See online at https://doi.org/10.1093/ije/dyw341)
  • Separators and Adjustment Sets in Markov Equivalent DAGs, In Proc. of the 30th AAAI Conference on Artificial Intelligence, (AAAI’16), 3315-3321, 2016
    Benito van der Zander, Maciej Liśkiewicz
    (See online at https://doi.org/10.1609/aaai.v30i1.10424)
  • Finding Minimal d-separators in Linear Time and Applications, In Proc. of the 35th Conference on Uncertainty in Artificial Intelligence (UAI’19), 637-647, 2019
    Benito van der Zander, Maciej Liśkiewicz
  • Separators and adjustment sets in s causal graphs: Complete criteria and an algorithmic framework, Artificial Intelligence, 270: 1-40 (2019)
    Benito van der Zander, Maciej Liśkiewicz, Johannes Textor
    (See online at https://doi.org/10.1016/j.artint.2018.12.006)
  • Recovering Causal Structures from Low-Order Conditional Independencies., In Proc. of the 34th AAAI Conference on Artificial Intelligence, (AAAI’20), 35(13), 10302-10309, 2020
    Marcel Wienöbst, Maciej Liśkiewicz
    (See online at https://doi.org/10.1609/aaai.v34i06.6593)
  • Extendability of Causal Graphical Models: Algorithms and Computational Complexity, In Proc. of the 37th Conference on Uncertainty in Artificial Intelligence (UAI’21), 1248-1257, 2021 [Best Student Paper]
    Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz
    (See online at https://dx.doi.org/10.48448/zh4w-yf08)
  • Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs, In Proc. of the 35th AAAI Conference on Artificial Intelligence, (AAAI’21), 12198-12206, 2021 [Distinguished Paper Award]
    Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz
    (See online at https://doi.org/10.1609/aaai.v35i13.17448)
 
 

Additional Information

Textvergrößerung und Kontrastanpassung