Causal inference with multiple time series: principles and problems

DOI10.1098/rsta.2011.0613zbMath1353.62094OpenAlexW2117259173WikidataQ38121876 ScholiaQ38121876MaRDI QIDQ2955526

Michael Eichler

Publication date: 13 January 2017

Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1098/rsta.2011.0613

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Foundations and philosophical topics in statistics (62A01)

Related Items (9)

Markov equivalence of marginalized local independence graphs ⋮ Graphical modeling of stochastic processes driven by correlated noise ⋮ Consistent causal inference from time series with PC algorithm and its time-aware extension ⋮ The impact of latent confounders in directed network analysis in neuroscience ⋮ Assessing causality in brain dynamics and cardiovascular control ⋮ Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions ⋮ Causal network reconstruction from time series: From theoretical assumptions to practical estimation ⋮ Bayesian nonparametric sparse VAR models ⋮ Cause-and-effect of linear mechanisms sustaining wall turbulence

Cites Work

• Unnamed Item
• Granger causality and path diagrams for multivariate time series
• Graphical modelling of multivariate time series
• On Granger causality and the effect of interventions in time series
• A new method of the description of the information flow in the brain structures
• Identification of vector AR models with recursive structural errors using conditional independence graphs
• Partial correlation analysis for the identification of synaptic connections
• Partial directed coherence: a new concept in neural structure determination
• Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance
• On the evaluation of information flow in multivariate systems by the directed transfer function
• The impact of latent confounders in directed network analysis in neuroscience
• Causal diagrams for empirical research
• Influence Diagrams for Causal Modelling and Inference
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Composable Markov processes

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