Kausalität: algorithmischer Ansatz und komplexitätstheoretische Perspektive

• Antragsteller: Professor Dr. Maciej Liskiewicz
• Fachliche Zuordnung: Theoretische Informatik
• Förderung: Förderung von 2016 bis 2022
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 273587939

• Erstellungsjahr: 2021

Zusammenfassung der Projektergebnisse

Keine Zusammenfassung vorhanden

Projektbezogene Publikationen (Auswahl)

• 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
  (Siehe online unter 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
  (Siehe online unter 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
  (Siehe online unter 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
  (Siehe online unter 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
  (Siehe online unter 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
  (Siehe online unter https://doi.org/10.1609/aaai.v35i13.17448)