Deep Recurrent Modelling of Granger Causality with Latent Confounding

• URL: http://arxiv.org/abs/2202.11286v1
• Date: Wed, 23 Feb 2022 03:26:22 GMT
• Title: Deep Recurrent Modelling of Granger Causality with Latent Confounding
• Authors: Zexuan Yin and Paolo Barucca
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer potential causal mechanisms between different time series. The original definition of Granger causality is restricted to linear processes and leads to spurious conclusions in the presence of a latent confounder. In this work, we harness the expressive power of recurrent neural networks and propose a deep learning-based approach to model non-linear Granger causality by directly accounting for latent confounders. Our approach leverages multiple recurrent neural networks to parameterise predictive distributions and we propose the novel use of a dual-decoder setup to conduct the Granger tests. We demonstrate the model performance on non-linear stochastic time series for which the latent confounder influences the cause and effect with different time lags; results show the effectiveness of our model compared to existing benchmarks.

Related papers

• A Non-negative VAE:the Generalized Gamma Belief Network [49.970917207211556]
  The gamma belief network (GBN) has demonstrated its potential for uncovering multi-layer interpretable latent representations in text data. We introduce the generalized gamma belief network (Generalized GBN) in this paper, which extends the original linear generative model to a more expressive non-linear generative model. We also propose an upward-downward Weibull inference network to approximate the posterior distribution of the latent variables.
  arXiv  Detail & Related papers  (2024-08-06T18:18:37Z)
• Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data [21.697069894721448]
  We present a theoretically-grounded method that infers Granger causal structure and identifies unknown targets by leveraging heterogeneous interventional time series data. Our method outperforms several robust baseline methods in learning Granger causal structure from interventional time series data.
  arXiv  Detail & Related papers  (2024-06-14T21:36:00Z)
• Jacobian Regularizer-based Neural Granger Causality [45.902407376192656]
  We propose a Jacobian Regularizer-based Neural Granger Causality (JRNGC) approach. Our method eliminates the sparsity constraints of weights by leveraging an input-output Jacobian matrix regularizer. Our proposed approach achieves competitive performance with the state-of-the-art methods for learning summary Granger causality and full-time Granger causality.
  arXiv  Detail & Related papers  (2024-05-14T17:13:50Z)
• Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery [52.95935278819512]
  We conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference of response generation model.
  arXiv  Detail & Related papers  (2023-03-02T06:33:48Z)
• Causal Graph Discovery from Self and Mutually Exciting Time Series [10.410454851418548]
  We develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program. We demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs)
  arXiv  Detail & Related papers  (2023-01-26T16:15:27Z)
• Interpretable Social Anchors for Human Trajectory Forecasting in Crowds [84.20437268671733]
  We propose a neural network-based system to predict human trajectory in crowds. We learn interpretable rule-based intents, and then utilise the expressibility of neural networks to model scene-specific residual. Our architecture is tested on the interaction-centric benchmark TrajNet++.
  arXiv  Detail & Related papers  (2021-05-07T09:22:34Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Interpretable Models for Granger Causality Using Self-explaining Neural Networks [4.56877715768796]
  We propose a novel framework for inferring Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality.
  arXiv  Detail & Related papers  (2021-01-19T12:59:00Z)
• Causal Modeling with Stochastic Confounders [11.881081802491183]
  This work extends causal inference with confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a random input space.
  arXiv  Detail & Related papers  (2020-04-24T00:34:44Z)
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [103.14809802212535]
  We build on the generative adversarial networks (GANs) framework to address the problem of estimating the effect of continuous-valued interventions. Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions. To address the challenges presented by shifting to continuous interventions, we propose a novel architecture for our discriminator.
  arXiv  Detail & Related papers  (2020-02-27T18:46:21Z)

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.