Learning linear acyclic causal model including Gaussian noise using ancestral relationships

• URL: http://arxiv.org/abs/2409.00417v1
• Date: Sat, 31 Aug 2024 11:07:15 GMT
• Title: Learning linear acyclic causal model including Gaussian noise using ancestral relationships
• Authors: Ming Cai, Penggang Gao, Hisayuki Hara,
• Abstract summary: LiNGAM assumes linearity and continuous non-Gaussian disturbances for the causal model. The PC algorithm makes no assumptions other than the faithfulness to the causal model. We propose an algorithm for learning the distribution-equivalence patterns of a linear causal model with a lower time complexity.
• Score: 6.340689966560241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper discusses algorithms for learning causal DAGs. The PC algorithm makes no assumptions other than the faithfulness to the causal model and can identify only up to the Markov equivalence class. LiNGAM assumes linearity and continuous non-Gaussian disturbances for the causal model, and the causal DAG defining LiNGAM is shown to be fully identifiable. The PC-LiNGAM, a hybrid of the PC algorithm and LiNGAM, can identify up to the distribution-equivalence pattern of a linear causal model, even in the presence of Gaussian disturbances. However, in the worst case, the PC-LiNGAM has factorial time complexity for the number of variables. In this paper, we propose an algorithm for learning the distribution-equivalence patterns of a linear causal model with a lower time complexity than PC-LiNGAM, using the causal ancestor finding algorithm in Maeda and Shimizu, which is generalized to account for Gaussian disturbances.

Related papers

• Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms [59.15672758767244]
  Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity.<n>Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions.<n>We propose several multi-view causal discovery algorithms, inspired by single-view algorithms.
  arXiv  Detail & Related papers  (2025-02-27T14:06:14Z)
• 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)
• On the Complexity of Identification in Linear Structural Causal Models [3.44747819522562]
  We give a new sound and complete algorithm for generic identification which runs in space. The paper also presents evidence that identification is computationally hard in general.
  arXiv  Detail & Related papers  (2024-07-17T13:11:26Z)
• Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models [2.0738462952016232]
  Methods based on functional causal models can identify a unique graph, but suffer from the curse of dimensionality or impose strong parametric assumptions. We propose a novel hybrid approach for global causal discovery in observational data that leverages local causal substructures. We provide theoretical guarantees for correctness and worst-case time complexities, with empirical validation on synthetic data.
  arXiv  Detail & Related papers  (2024-05-23T12:28:16Z)
• TSLiNGAM: DirectLiNGAM under heavy tails [0.0]
  We propose TSLiNGAM, a new method for identifying the DAG of a causal model based on observational data. TSLiNGAM builds on DirectLiNGAM, a popular algorithm which uses simple OLS regression for identifying causal directions between variables. It performs significantly better on heavy-tailed and skewed data and demonstrates a high small-sample efficiency.
  arXiv  Detail & Related papers  (2023-08-10T08:34:46Z)
• Causal Discovery with Score Matching on Additive Models with Arbitrary Noise [37.13308785728276]
  Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. In this paper we show the shortcomings of inference under this hypothesis, analyzing the risk of edge inversion under violation of Gaussianity of the noise terms. We propose a novel method for inferring the topological ordering of the variables in the causal graph, from data generated according to an additive non-linear model with a generic noise distribution. This leads to NoGAM, a causal discovery algorithm with a minimal set of assumptions and state of the art performance, experimentally benchmarked on synthetic data.
  arXiv  Detail & Related papers  (2023-04-06T17:50:46Z)
• Learning Graphical Factor Models with Riemannian Optimization [70.13748170371889]
  This paper proposes a flexible algorithmic framework for graph learning under low-rank structural constraints. The problem is expressed as penalized maximum likelihood estimation of an elliptical distribution. We leverage geometries of positive definite matrices and positive semi-definite matrices of fixed rank that are well suited to elliptical models.
  arXiv  Detail & Related papers  (2022-10-21T13:19:45Z)
• MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models [78.72682320019737]
  We develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations. MissDAG maximizes the expected likelihood of the visible part of observations under the expectation-maximization framework. We demonstrate the flexibility of MissDAG for incorporating various causal discovery algorithms and its efficacy through extensive simulations and real data experiments.
  arXiv  Detail & Related papers  (2022-05-27T09:59:46Z)
• Sequential Learning of the Topological Ordering for the Linear Non-Gaussian Acyclic Model with Parametric Noise [6.866717993664787]
  We develop a novel sequential approach to estimate the causal ordering of a DAG. We provide extensive numerical evidence to demonstrate that our procedure is scalable to cases with possibly thousands of nodes.
  arXiv  Detail & Related papers  (2022-02-03T18:15:48Z)
• Partial Counterfactual Identification from Observational and Experimental Data [83.798237968683]
  We develop effective Monte Carlo algorithms to approximate the optimal bounds from an arbitrary combination of observational and experimental data. Our algorithms are validated extensively on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-10-12T02:21:30Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Learning Gaussian Graphical Models via Multiplicative Weights [54.252053139374205]
  We adapt an algorithm of Klivans and Meka based on the method of multiplicative weight updates. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature. It has a low runtime $O(mp2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
  arXiv  Detail & Related papers  (2020-02-20T10:50:58Z)

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.