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.…
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 |