Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data

• URL: http://arxiv.org/abs/2310.20537v1
• Date: Tue, 31 Oct 2023 15:19:24 GMT
• Title: Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
• Authors: Saptarshi Roy, Raymond K. W. Wong, Yang Ni
• Abstract summary: We introduce a functional linear structural equation model for causal structure learning. To enhance interpretability, our model involves a low-dimensional causal embedded space. We prove that the proposed model is causally identifiable under standard assumptions.
• Score: 15.26007975367927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discovering causal relationship using multivariate functional data has received a significant amount of attention very recently. In this article, we introduce a functional linear structural equation model for causal structure learning when the underlying graph involving the multivariate functions may have cycles. To enhance interpretability, our model involves a low-dimensional causal embedded space such that all the relevant causal information in the multivariate functional data is preserved in this lower-dimensional subspace. We prove that the proposed model is causally identifiable under standard assumptions that are often made in the causal discovery literature. To carry out inference of our model, we develop a fully Bayesian framework with suitable prior specifications and uncertainty quantification through posterior summaries. We illustrate the superior performance of our method over existing methods in terms of causal graph estimation through extensive simulation studies. We also demonstrate the proposed method using a brain EEG dataset.

Related papers

• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges by developing an textitinfluence functions framework.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Induced Covariance for Causal Discovery in Linear Sparse Structures [55.2480439325792]
  Causal models seek to unravel the cause-effect relationships among variables from observed data. This paper introduces a novel causal discovery algorithm designed for settings in which variables exhibit linearly sparse relationships.
  arXiv  Detail & Related papers  (2024-10-02T04:01:38Z)
• Functional Linear Non-Gaussian Acyclic Model for Causal Discovery [7.303542369216906]
  We develop a framework to identify causal relationships in brain-effective connectivity tasks involving fMRI and EEG datasets. We establish theoretical guarantees of the identifiability of the causal relationship among non-Gaussian random vectors and even random functions in infinite-dimensional Hilbert spaces. For real data, we focus on analyzing the brain connectivity patterns derived from fMRI data.
  arXiv  Detail & Related papers  (2024-01-17T23:27:48Z)
• SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling [3.988614978933934]
  Causal inference is a crucial goal of science, enabling researchers to arrive at meaningful conclusions using observational data. Path models, Structural Equation Models (SEMs) and Directed Acyclic Graphs (DAGs) provide a means to unambiguously specify assumptions regarding the causal structure underlying a phenomenon. We propose Super Learner Equation Modeling, a path modeling technique integrating machine learning Super Learner ensembles.
  arXiv  Detail & Related papers  (2023-08-08T16:04:42Z)
• Latent Multimodal Functional Graphical Model Estimation [26.457941699285165]
  We propose a new framework that models the data generation process and identifies operators mapping from the observation space to the latent space. We then develop an estimator that simultaneously estimates the transformation operators and the latent graph. Our work is applied to analyze simultaneously acquired multimodal brain imaging data where the graph indicates functional connectivity of the brain.
  arXiv  Detail & Related papers  (2022-10-31T11:43:05Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs [77.34726150561087]
  We introduce the R package for causal discovery and causal effect estimation from observational data. Our implementation scales efficiently with the number of observations and, whenever the DAGs are sufficiently sparse, the number of variables in the dataset. We then illustrate the main functions and algorithms on both real and simulated datasets.
  arXiv  Detail & Related papers  (2022-01-28T09:30:32Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis Systems [2.4554686192257424]
  We propose a framework for learning continuous representations from a sample of multidimensional functional data. We show that the resulting estimation problem can be solved efficiently by the tensor decomposition. We conclude with a real data application in neuroimaging.
  arXiv  Detail & Related papers  (2021-07-30T16:02:15Z)
• Causal Inference with Deep Causal Graphs [0.0]
  Parametric causal modelling techniques rarely provide functionality for counterfactual estimation. Deep Causal Graphs is an abstract specification of the required functionality for a neural network to model causal distributions. We demonstrate its expressive power in modelling complex interactions and showcase applications to machine learning explainability and fairness.
  arXiv  Detail & Related papers  (2020-06-15T13:03:33Z)
• Bayesian Sparse Factor Analysis with Kernelized Observations [67.60224656603823]
  Multi-view problems can be faced with latent variable models. High-dimensionality and non-linear issues are traditionally handled by kernel methods. We propose merging both approaches into single model.
  arXiv  Detail & Related papers  (2020-06-01T14:25:38Z)

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.