Granger Causality in Extremes

• URL: http://arxiv.org/abs/2407.09632v2
• Date: Fri, 18 Oct 2024 03:31:01 GMT
• Title: Granger Causality in Extremes
• Authors: Juraj Bodik, Olivier C. Pasche,
• Abstract summary: We introduce a rigorous framework for identifying causal links from extreme events in time series. Our framework is designed to infer causality mainly from extreme events by leveraging the causal tail coefficient. We also propose a novel inference method for detecting the presence of Granger causality in extremes from data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pivotal role in uncovering directional relationships among time-varying variables. While this notion gains heightened importance during extreme and highly volatile periods, state-of-the-art methods primarily focus on causality within the body of the distribution, often overlooking causal mechanisms that manifest only during extreme events. Our framework is designed to infer causality mainly from extreme events by leveraging the causal tail coefficient. We establish equivalences between causality in extremes and other causal concepts, including (classical) Granger causality, Sims causality, and structural causality. We prove other key properties of Granger causality in extremes and show that the framework is especially helpful under the presence of hidden confounders. We also propose a novel inference method for detecting the presence of Granger causality in extremes from data. Our method is model-free, can handle non-linear and high-dimensional time series, outperforms current state-of-the-art methods in all considered setups, both in performance and speed, and was found to uncover coherent effects when applied to financial and extreme weather observations.

Related papers

• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes [24.956802640469554]
  Instance-wise Self-Attentive Hawkes Processes (ISAHP) is a novel deep learning framework that can directly infer the Granger causality at the instance level. ISAHP is capable of discovering complex instance-level causal structures that cannot be handled by classical models.
  arXiv  Detail & Related papers  (2024-02-06T05:46:51Z)
• A Measure-Theoretic Axiomatisation of Causality [55.6970314129444]
  We argue in favour of taking Kolmogorov's measure-theoretic axiomatisation of probability as the starting point towards an axiomatisation of causality. Our proposed framework is rigorously grounded in measure theory, but it also sheds light on long-standing limitations of existing frameworks.
  arXiv  Detail & Related papers  (2023-05-19T13:15:48Z)
• Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences [36.76562861154494]
  The proposed method is featured with the minorization-maximization of the likelihood function and a sparse optimization scheme. Results show that the instantaneous effect is a blessing rather than a curse, and the causal structure is identifiable under the existence of the instantaneous effect.
  arXiv  Detail & Related papers  (2023-05-10T08:52:07Z)
• Deep Recurrent Modelling of Granger Causality with Latent Confounding [0.0]
  We propose a deep learning-based approach to model non-linear Granger causality by directly accounting for latent confounders. We demonstrate the model performance on non-linear time series for which the latent confounder influences the cause and effect with different time lags.
  arXiv  Detail & Related papers  (2022-02-23T03:26:22Z)
• Causal Inference Principles for Reasoning about Commonsense Causality [93.19149325083968]
  Commonsense causality reasoning aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person. Existing work usually relies on deep language models wholeheartedly, and is potentially susceptible to confounding co-occurrences. Motivated by classical causal principles, we articulate the central question of CCR and draw parallels between human subjects in observational studies and natural languages. We propose a novel framework, ROCK, to Reason O(A)bout Commonsense K(C)ausality, which utilizes temporal signals as incidental supervision.
  arXiv  Detail & Related papers  (2022-01-31T06:12:39Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• Granger Causality: A Review and Recent Advances [10.66048003460524]
  Granger causality has become a popular tool for analyzing time series data in many application domains. This paper discusses recent advances that address various shortcomings of the earlier approaches.
  arXiv  Detail & Related papers  (2021-05-05T17:37:18Z)
• Inductive Granger Causal Modeling for Multivariate Time Series [49.29373497269468]
  We propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection. In particular, we train one global model for individuals with different Granger causal structures through a novel attention mechanism, called Granger causal attention. The model can detect common causal structures for different individuals and infer Granger causal structures for newly arrived individuals.
  arXiv  Detail & Related papers  (2021-02-10T07:48:00Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis [7.627597166844701]
  We develop Variable-lag Granger causality and Variable-lag Transfer Entropy, which relax the assumption of the fixed time delay. In our approaches, we utilize an optimal warping path of Dynamic Time Warping (DTW) to infer variable-lag causal relations. Our approaches can be applied in any domain of time series analysis.
  arXiv  Detail & Related papers  (2020-02-01T14:03:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.