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Discovering instantaneous and lagged causal relations in autocorrelated nonlinear time series datasets

Runge, Jakob (2020) Discovering instantaneous and lagged causal relations in autocorrelated nonlinear time series datasets. UAI 2020, Online.

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Abstract

The paper introduces a novel conditional in- dependence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. Existing CI-based methods such as the PC algorithm and also common methods from other frameworks suf- fer from low recall and partially inflated false positives for strong autocorrelation which is an ubiquitous challenge in time series. The novel method, PCMCI + , extends PCMCI [Runge et al., 2019b] to include discovery of contempo- raneous links. PCMCI + improves the relia- bility of CI tests by optimizing the choice of conditioning sets and even benefits from auto- correlation. The method is order-independent and consistent in the oracle case. A broad range of numerical experiments demonstrates that PCMCI + has higher adjacency detec- tion power and especially more contempo- raneous orientation recall compared to other methods while better controlling false posi- tives. Optimized conditioning sets also lead to much shorter runtimes than the PC algorithm. PCMCI + can be of considerable use in many real world application scenarios where often time resolutions are too coarse to resolve time delays and strong autocorrelation is present.

Item URL in elib:https://elib.dlr.de/139177/
Document Type:Conference or Workshop Item (Speech)
Title:Discovering instantaneous and lagged causal relations in autocorrelated nonlinear time series datasets
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Runge, JakobUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2020
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:none
Event Title:UAI 2020
Event Location:Online
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science
Deposited By: Käding, Christoph
Deposited On:04 Dec 2020 12:48
Last Modified:04 Dec 2020 12:48

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