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Comparing Causal Discovery Methods using Synthetic and Real Data

Käding, Christoph und Runge, Jakob (2020) Comparing Causal Discovery Methods using Synthetic and Real Data. EGU 2020, 2020-05-04 - 2020-05-08, Online.

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Offizielle URL: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-9269.html

Kurzfassung

Unveiling causal structures, i.e., distinguishing cause from effect, from observational data plays a key role in climate science as well as in other fields like medicine or economics. Hence, a number of approaches has been developed to approach this. Recent decades have seen methods like Granger causality or causal network learning algorithms, which are, however, not generally applicable in every scenario. When given two variables X and Y, it is still a challenging problem to decide whether X causes Y, or Y causes X. Recently, there has been progress in the framework of structural causal models, which enable the discovery of causal relationships by making use of functional dependencies (e.g., only linear) and noise models (e.g., only non-Gaussian noise). However, each of them is coming with its own requirements and constraints. While the corresponding conditions are usually unknown in real scenarios, it is quite hard to choose the right method for every application in general. The goal of this work is to evaluate and to compare a number of state-of-the-art techniques in a joint benchmark. To do so, we employ synthetic data, where we can control for the dataset conditions precisely, and hence can give detailed reasoning about the resulting performance of the individual methods given their underlying assumptions. Further, we utilize real-world data to shed light on their capabilities in actual applications in a comparative manner. We concentrate on the case considering two uni-variate variables due to the large number of possible application scenarios. A profound study, comparing even the latest developments, is, to the best of our knowledge, so far not available in the literature.

elib-URL des Eintrags:https://elib.dlr.de/135968/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Comparing Causal Discovery Methods using Synthetic and Real Data
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Käding, ChristophChristoph.Kaeding (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Runge, JakobJakob.Runge (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Mai 2020
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Causal Discovery, Benchmark, Causality
Veranstaltungstitel:EGU 2020
Veranstaltungsort:Online
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:4 Mai 2020
Veranstaltungsende:8 Mai 2020
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:keine Zuordnung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R - keine Zuordnung
DLR - Teilgebiet (Projekt, Vorhaben):R - keine Zuordnung
Standort: Jena
Institute & Einrichtungen:Institut für Datenwissenschaften
Hinterlegt von: Käding, Christoph
Hinterlegt am:01 Dez 2020 14:49
Letzte Änderung:24 Jun 2024 12:24

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