Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps

Reimers, Christian and Runge, Jakob and Denzler, Joachim (2020) Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps. In: Proceedings of the 9th International Workshop on Climate Informatics: CI 2019, pp. 1-4. 9th Int. Work. Clim. Informatics, Paris.

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Item URL in elib:

https://elib.dlr.de/131637/

Document Type:

Conference or Workshop Item (Lecture)

Title:

Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Reimers, Christian	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Runge, Jakob	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Denzler, Joachim	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED

Date:

7 January 2020

Journal or Publication Title:

Proceedings of the 9th International Workshop on Climate Informatics: CI 2019

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Page Range:

pp. 1-4

Status:

Published

Keywords:

climate informatics

Event Title:

9th Int. Work. Clim. Informatics

Event Location:

Paris

Event Type:

international Conference

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Aeronautics

HGF - Program Themes:

air traffic management and operations

DLR - Research area:

Aeronautics

DLR - Program:

L AO - Air Traffic Management and Operation

DLR - Research theme (Project):

L - Climate, Weather and Environment (old)

Location:

Jena

Institutes and Institutions:

Institute of Data Science > Datamangagement and Analysis

Deposited By:

Runge, Jakob

Deposited On:

07 Jan 2020 13:28

Last Modified:

07 Jan 2020 13:28

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