DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

High-recall causal discovery for autocorrelated time series with latent confounders

Gerhardus, Andreas and Runge, Jakob (2020) High-recall causal discovery for autocorrelated time series with latent confounders. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), 06.-12.12.2020, Online.

[img] PDF
[img] PDF

Official URL: https://papers.nips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html


We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of conditional independence tests as the main reason. Information-theoretical arguments show that effect size can often be increased if causal parents are included in the conditioning sets. To identify parents early on, we suggest an iterative procedure that utilizes novel orientation rules to determine ancestral relationships already during the edge removal phase. We prove that the method is order-independent, and sound and complete in the oracle case. Extensive simulation studies for different numbers of variables, time lags, sample sizes, and further cases demonstrate that our method indeed achieves much higher recall than existing methods for the case of autocorrelated continuous variables while keeping false positives at the desired level. This performance gain grows with stronger autocorrelation. At github.com/jakobrunge/tigramite we provide Python code for all methods involved in the simulation studies.

Item URL in elib:https://elib.dlr.de/138893/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:High-recall causal discovery for autocorrelated time series with latent confounders
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Gerhardus, AndreasUNSPECIFIEDhttps://orcid.org/0000-0003-1868-655XUNSPECIFIED
Runge, JakobUNSPECIFIEDhttps://orcid.org/0000-0002-0629-1772UNSPECIFIED
Date:December 2020
Journal or Publication Title:Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:causal discovery, time series analysis, causal inference, causality, machine learning, hidden variables
Event Title:Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020)
Event Location:Online
Event Type:international Conference
Event Dates:06.-12.12.2020
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 > Datamangagement and Analysis
Deposited By: Gerhardus, Andreas
Deposited On:04 Mar 2021 14:51
Last Modified:04 Mar 2021 14:51

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.