Review on Causality Detection Based on Empirical Dynamic Modeling

• URL: http://arxiv.org/abs/2312.15919v1
• Date: Tue, 26 Dec 2023 07:25:12 GMT
• Title: Review on Causality Detection Based on Empirical Dynamic Modeling
• Authors: Cao Zhihao, Qu Hongchun
• Abstract summary: Empirical Dynamic Modeling emerges as a data-driven framework for modeling dynamic systems. This paper explores the detection of causal relationships between variables within dynamic systems through their time series data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In contemporary scientific research, understanding the distinction between correlation and causation is crucial. While correlation is a widely used analytical standard, it does not inherently imply causation. This paper addresses the potential for misinterpretation in relying solely on correlation, especially in the context of nonlinear dynamics. Despite the rapid development of various correlation research methodologies, including machine learning, the exploration into mining causal correlations between variables remains ongoing. Empirical Dynamic Modeling (EDM) emerges as a data-driven framework for modeling dynamic systems, distinguishing itself by eschewing traditional formulaic methods in data analysis. Instead, it reconstructs dynamic system behavior directly from time series data. The fundamental premise of EDM is that dynamic systems can be conceptualized as processes where a set of states, governed by specific rules, evolve over time in a high-dimensional space. By reconstructing these evolving states, dynamic systems can be effectively modeled. Using EDM, this paper explores the detection of causal relationships between variables within dynamic systems through their time series data. It posits that if variable X causes variable Y, then the information about X is inherent in Y and can be extracted from Y's data. This study begins by examining the dialectical relationship between correlation and causation, emphasizing that correlation does not equate to causation, and the absence of correlation does not necessarily indicate a lack of causation.

Related papers

• Assimilative Causal Inference [1.4322470793889193]
  Causal inference determines cause-and-effect relationships between variables.<n>A new causal inference framework, called assimilative causal inference (ACI), is developed.
  arXiv  Detail & Related papers  (2025-05-20T18:40:17Z)
• No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
  This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process. Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system. Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
  arXiv  Detail & Related papers  (2025-01-30T18:36:48Z)
• Dynamic Causal Structure Discovery and Causal Effect Estimation [5.943525863330208]
  We develop a new framework to model the dynamic causal graph where the causal relations are allowed to be time-varying. We propose an algorithm that could provide both past-time estimates and future-time predictions on the causal graphs.
  arXiv  Detail & Related papers  (2025-01-11T12:52:39Z)
• Revisiting Spurious Correlation in Domain Generalization [12.745076668687748]
  We build a structural causal model (SCM) to describe the causality within data generation process. We further conduct a thorough analysis of the mechanisms underlying spurious correlation. In this regard, we propose to control confounding bias in OOD generalization by introducing a propensity score weighted estimator.
  arXiv  Detail & Related papers  (2024-06-17T13:22:00Z)
• Spurious Correlations and Where to Find Them [17.1264393170134]
  Spurious correlations occur when a model learns unreliable features from the data. We collect some of the commonly studied hypotheses behind the occurrence of spurious correlations. We investigate their influence on standard ERM baselines using synthetic datasets generated from causal graphs.
  arXiv  Detail & Related papers  (2023-08-21T21:06:36Z)
• Causal discovery for time series with constraint-based model and PMIME measure [0.0]
  We present a novel approach for discovering causality in time series data that combines a causal discovery algorithm with an information theoretic-based measure. We evaluate the performance of our approach on several simulated data sets, showing promising results.
  arXiv  Detail & Related papers  (2023-05-31T09:38:50Z)
• Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA [81.4991350761909]
  Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application. We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
  arXiv  Detail & Related papers  (2021-07-21T14:22:14Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)
• Estimating Causal Effects with the Neural Autoregressive Density Estimator [6.59529078336196]
  We use neural autoregressive density estimators to estimate causal effects within the Pearl's do-calculus framework. We show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables.
  arXiv  Detail & Related papers  (2020-08-17T13:12:38Z)
• On Disentangled Representations Learned From Correlated Data [59.41587388303554]
  We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data. We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
  arXiv  Detail & Related papers  (2020-06-14T12:47:34Z)
• Learning Causal Models Online [103.87959747047158]
  Predictive models can rely on spurious correlations in the data for making predictions. One solution for achieving strong generalization is to incorporate causal structures in the models. We propose an online algorithm that continually detects and removes spurious features.
  arXiv  Detail & Related papers  (2020-06-12T20:49:20Z)

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.