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causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery

  • 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, we introduce causalAssembly, a semisynthetic data generator designed to facilitate the benchmarking of causal discovery methods. The tool is built using a complex real-world dataset comprised of measurements collected along an assembly line in a manufacturing setting. For these measurements, we establish a partial set of ground truth causal relationships through a detailed study of the physics underlying the processes carried out in the assembly line. The partial ground truth is sufficiently informative to allow for estimation of a fullAlgorithms 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, we introduce causalAssembly, a semisynthetic data generator designed to facilitate the benchmarking of causal discovery methods. The tool is built using a complex real-world dataset comprised of measurements collected along an assembly line in a manufacturing setting. For these measurements, we establish a partial set of ground truth causal relationships through a detailed study of the physics underlying the processes carried out in the assembly line. The partial ground truth is sufficiently informative to allow for estimation of a full causal graph by mere nonparametric regression. To overcome potential confounding and privacy concerns, we use distributional random forests to estimate and represent conditional distributions implied by the ground truth causal graph. These conditionals are combined into a joint distribution that strictly adheres to a causal model over the observed variables. Sampling from this distribution, causalAssembly generates data that are guaranteed to be Markovian with respect to the ground truth. Using our tool, we showcase how to benchmark several well-known causal discovery algorithms.show moreshow less

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Metadaten
Author:Konstantin Göbler, Tobias WindischORCiD, Mathias DrtonORCiD, Tim Pychynski, Steffen Sonntag, Martin Roth
URL / DOI:https://proceedings.mlr.press/v236/gobler24a.html
Identifier:2640-3498 OPAC HS OPAC extern
Parent Title (English):Proceedings of Machine Learning Research
Publisher:Proceedings of Machine Learning Research
Place of publication:Red Hook, NY
Editor:Francesco Locatello, Vanessa Didelez
Document Type:conference proceeding (article)
Conference:3rd Conference on Causal Learning and Reasoning, 1-3 April 2024, Los Angeles, California, USA
Language:English
Date of Publication (online):2024/04/01
Year of first Publication:2024
Tag:Causal discovery; benchmarking; distributional random forest; production data
Volume:236
Number of pages:34 Seiten
First Page:609
Last Page:642
Institutes:Fakultät Maschinenbau
IMS - Institut für Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Research focus:FSP3: Produktion
Publication Lists:Windisch, Tobias
Publication reviewed:nicht begutachtet
Release Date:2024/07/17
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