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Learning cause-and-effect relationships from time series data

Gerhardus, Andreas (2021) Learning cause-and-effect relationships from time series data. KITP Program: Machine Learning and the Physics of Climate, Santa Barbara, USA. doi: 10.26081/K6SP99.

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Official URL: https://online.kitp.ucsb.edu/online/climate21/

Abstract

In the first part of this talk we will give a brief introduction into the modern causal inference framework for reasoning about cause-and-effect relationships from observational data. The focus will be on the causal discovery problem, i.e., data-driven learning of qualitative causal relationships. In the second part we will introduce two recently developed algorithms for causal discovery in time series. The first of these, LPCMCI, extends the applicability of previous algorithms by specifically allowing for unobserved confounders. The second, Ensemble-PCMCI, performs causal discovery on ensembles of time series and thus allows to relax the usually made assumption of stationarity.

Item URL in elib:https://elib.dlr.de/146280/
Document Type:Conference or Workshop Item (Speech)
Title:Learning cause-and-effect relationships from time series data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Gerhardus, AndreasAndreas.Gerhardus (at) dlr.dehttps://orcid.org/0000-0003-1868-655X
Date:November 2021
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.26081/K6SP99
Status:Published
Keywords:causal inference, causal discovery, causality, time series, hidden variables, non-stationarity
Event Title:KITP Program: Machine Learning and the Physics of Climate
Event Location:Santa Barbara, USA
Event Type:Workshop
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Climate Informatics
Location: Jena
Institutes and Institutions:Institute of Data Science > Datamangagement and Analysis
Deposited By: Gerhardus, Andreas
Deposited On:30 Nov 2021 12:07
Last Modified:30 Nov 2021 12:07

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