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Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

Authors:

Aapo Hyvärinen,

Shohei Shimizu,

Patrik O. HoyerAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 424 - 431

https://doi.org/10.1145/1390156.1390210

Published: 05 July 2008 Publication History

Abstract

Causal analysis of continuous-valued variables typically uses either autoregressive models or linear Gaussian Bayesian networks with instantaneous effects. Estimation of Gaussian Bayesian networks poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model with autoregressive models. We show that such a non-Gaussian model is identifiable without prior knowledge of network structure, and we propose an estimation method shown to be consistent. This approach also points out how neglecting instantaneous effects can lead to completely wrong estimates of the autoregressive coefficients.

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Cited By

Pichot VCorbier CChouchou F(2024)The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network controlFrontiers in Network Physiology10.3389/fnetp.2024.13153164Online publication date: 8-Aug-2024
https://doi.org/10.3389/fnetp.2024.1315316
Gong CZhang CYao DBi JLi WXu Y(2024)Causal Discovery from Temporal Data: An Overview and New PerspectivesACM Computing Surveys10.1145/370529757:4(1-38)Online publication date: 23-Nov-2024
https://dl.acm.org/doi/10.1145/3705297
Chen SWu HJin G(2024)Causal structure learning for high-dimensional non-stationary time seriesKnowledge-Based Systems10.1016/j.knosys.2024.111868295(111868)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111868
Show More Cited By

Index Terms

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies
2. Mathematics of computing
  1. Probability and statistics

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Information

Published In

cover image ACM Other conferences

ICML '08: Proceedings of the 25th international conference on Machine learning

July 2008

1310 pages

ISBN:9781605582054

DOI:10.1145/1390156

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Pascal
University of Helsinki
Xerox
Federation of Finnish Learned Societies
Google Inc.
NSF
Machine Learning Journal/Springer
Microsoft Research: Microsoft Research
Intel: Intel
Yahoo!
Helsinki Institute for Information Technology
IBM: IBM

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2008

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ICML '08

Sponsor:

Microsoft Research
Intel
IBM

ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Pichot VCorbier CChouchou F(2024)The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network controlFrontiers in Network Physiology10.3389/fnetp.2024.13153164Online publication date: 8-Aug-2024
https://doi.org/10.3389/fnetp.2024.1315316
Gong CZhang CYao DBi JLi WXu Y(2024)Causal Discovery from Temporal Data: An Overview and New PerspectivesACM Computing Surveys10.1145/370529757:4(1-38)Online publication date: 23-Nov-2024
https://dl.acm.org/doi/10.1145/3705297
Chen SWu HJin G(2024)Causal structure learning for high-dimensional non-stationary time seriesKnowledge-Based Systems10.1016/j.knosys.2024.111868295(111868)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111868
Leong KXiu YChen BChan W(2023)Neural Causal Information Extractor for Unobserved CausesEntropy10.3390/e2601004626:1(46)Online publication date: 31-Dec-2023
https://doi.org/10.3390/e26010046
Liu YCui CWaxman DButler KDjurić P(2023)Detecting Confounders in Multivariate Time Series using Strength of Causation2023 31st European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO58844.2023.10289850(1400-1404)Online publication date: 4-Sep-2023
https://doi.org/10.23919/EUSIPCO58844.2023.10289850
Suryadi Chew LOng Y(2023)Granger causality using Jacobian in neural networksChaos: An Interdisciplinary Journal of Nonlinear Science10.1063/5.010666633:2(023126)Online publication date: Feb-2023
https://doi.org/10.1063/5.0106666
Ciarli TCoad AMoneta A(2023)Does exporting cause productivity growth? Evidence from Chilean firmsStructural Change and Economic Dynamics10.1016/j.strueco.2023.04.01566(228-239)Online publication date: Sep-2023
https://doi.org/10.1016/j.strueco.2023.04.015
Huang SZeng YCai RHao ZSun F(2023)Offline Causal Imitation Learning with Latent ConfoundersCognitive Computation and Systems10.1007/978-981-99-2789-0_19(227-236)Online publication date: 24-May-2023
https://doi.org/10.1007/978-981-99-2789-0_19
Cai RHuang LChen WQiao JHao Z(2022)Learning dynamic causal mechanisms from non-stationary dataApplied Intelligence10.1007/s10489-022-03843-3Online publication date: 23-Jun-2022
https://doi.org/10.1007/s10489-022-03843-3
Jin ZGuo SChen NWeiskopf DGotz DCao N(2021)Visual Causality Analysis of Event Sequence DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303046527:2(1343-1352)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030465
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