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Titel:

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Vortrag / Präsentation
Autor(en):
Göbler, Konstantin; Windisch, Tobias; Drton, Mathias; Pychynski, Tim; Roth, Martin; Sonntag, Steffen
Seitenangaben Beitrag:
609--642
Abstract:
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real and complex data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To tackle these challenges...     »
Stichworte:
Causal discovery, benchmarking, production data, distributional random forest
Dewey-Dezimalklassifikation:
510 Mathematik
Herausgeber:
MLResearchPress
Kongress- / Buchtitel:
Proceedings of Machine Learning Research
Kongress / Zusatzinformationen:
Third Conference on Causal Learning and Reasoning
Band / Teilband / Volume:
236
Datum der Konferenz:
April 1-3, 2024
Publikationsdatum:
19.03.2024
Jahr:
2024
Quartal:
1. Quartal
Jahr / Monat:
2024-03
Monat:
Mar
E-ISBN:
2640-3498
Sprache:
en
Erscheinungsform:
WWW
WWW:
Proceedings of Machine Learning Research
Semester:
WS 23-24
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX