Fetching the paper…
Reading the bibliography…
We study the identification of direct and indirect causes on time series and provide conditions in the presence of latent variables, which we prove to be necessary and sufficient under some graph constraints.
The theory of prediction, Modern mathematics for the engineer , volume 8
Norbert Wiener · 1956
Earlier work this paper cites.
Investigating causal relations by econometric models and crossspectral methods
C. W. J Granger · 1969
Earlier work this paper cites.
Testing for causality, a personal viewpoint. , volume 2
C. W. J Granger · 1980
Earlier work this paper cites.
Causation, Prediction, and Search
P. Spirtes, C. Glymour, and R. Scheines · 1993
Earlier work this paper cites.
Causal inference from time series: What can be learned from Granger causality
Michael Eichler · 2007
Earlier work this paper cites.
Temporal causal modeling with Graphical Granger Methods
Andrew Arnold, Yan Liu, and Naoki Abe · 2007
Earlier work this paper cites.
Partial granger causality-Eliminating exogenous inputs and latent variables
Shuixia Guo, Anil K. Seth, Keith M. Kendrick, Cong Zhou, and Jianfeng Feng · 2008
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Cited alongside, same era.
On causal discovery from time series data using FCI
Doris Entner and Patrik O Hoyer · 2010
Cited alongside, same era.
Dairy marketing system performance in egypt
Ibrahim Soliman and Ahmed Mashhour · 2011
Cited alongside, same era.
Kernel-based conditional independence test and application in causal discovery
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2012
Cited alongside, same era.
Trimmed granger causality between two groups of time series
Ying-Chao Hung, Neng-Fang Tseng, and Narayanaswamy Balakrishnan · 2014
Cited alongside, same era.
A permutation-based kernel conditional independence test
G. Doran, K. Muandet, K. Zhang, and B. Schölkopf · 2014
Cited alongside, same era.
Causal structure learning from multivariate time series in settings with unmeasured confounding
Daniel Malinsky and Peter Spirtes · 2018
Later among the works it cites.
Causal network reconstruction from time series: From theoretical assumptions to practical estimation
J. Runge · 2018
Later among the works it cites.
Fast conditional independence test for vector variables with large sample sizes
Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt · 2018
Later among the works it cites.
Invariant causal prediction for sequential data
Niklas Pfister, Peter Bühlmann, and Jonas Peters · 2019
Later among the works it cites.
Selecting causal brain features with a single conditional independence test per feature
A. Mastakouri, B. Schölkopf, and D. Janzing · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Elements of Causal Inference - Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Inferring causation from time series in earth system sciences
Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D Mahecha, Jordi Muñoz-Marí, et al
Cited in the paper.
Detecting and quantifying causal associations in large nonlinear time series datasets
Jakob Runge, Peer Nowack, Marlene Kretschmer, Seth Flaxman, and Dino Sejdinovic
Cited in the paper.
European union prices of dairy products
EU
Cited in the paper.
Graphical criteria for efficient total effect estimation via adjustment in causal linear models
Leonard Henckel, Emilija Perković, and Marloes H. Maathuis · 2019
Later among the works it cites.