Learning Linear Gaussian Polytree Models with Interventions

• URL: http://arxiv.org/abs/2311.04636v1
• Date: Wed, 8 Nov 2023 12:29:19 GMT
• Title: Learning Linear Gaussian Polytree Models with Interventions
• Authors: D. Tramontano, L. Waldmann, M. Drton, and E. Duarte
• Abstract summary: We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree. Our methods first learn the skeleton of the polytree and then orient its edges. Our simulation studies demonstrate that our approach is fast, has good accuracy in terms of structural Hamming distance, and handles problems with thousands of nodes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree using data from interventional experiments with known intervention targets. Our methods first learn the skeleton of the polytree and then orient its edges. The output is a CPDAG representing the interventional equivalence class of the polytree of the true underlying distribution. The skeleton and orientation recovery procedures we use rely on second order statistics and low-dimensional marginal distributions. We assess the performance of our methods under different scenarios in synthetic data sets and apply our algorithm to learn a polytree in a gene expression interventional data set. Our simulation studies demonstrate that our approach is fast, has good accuracy in terms of structural Hamming distance, and handles problems with thousands of nodes.

Related papers

• 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)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Optimal estimation of Gaussian (poly)trees [25.02920605955238]
  We consider both problems of distribution learning (i.e. in KL distance) and structure learning (i.e. exact recovery) The first approach is based on the Chow-Liu algorithm, and learns an optimal tree-structured distribution efficiently. The second approach is a modification of the PC algorithm for polytrees that uses partial correlation as a conditional independence tester for constraint-based structure learning.
  arXiv  Detail & Related papers  (2024-02-09T12:58:36Z)
• Learning Linear Causal Representations from Interventions under General Nonlinear Mixing [52.66151568785088]
  We prove strong identifiability results given unknown single-node interventions without access to the intervention targets. This is the first instance of causal identifiability from non-paired interventions for deep neural network embeddings.
  arXiv  Detail & Related papers  (2023-06-04T02:32:12Z)
• Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions [3.441021278275805]
  We consider the problem of recovering the causal structure underlying observations when the targets of the interventions in each experiment are unknown. We derive a greedy algorithm called GnIES to recover the equivalence class of the data-generating model without knowledge of the intervention targets. We leverage this procedure and evaluate the performance of GnIES on synthetic, real, and semi-synthetic data sets.
  arXiv  Detail & Related papers  (2022-11-27T17:37:21Z)
• Learning Linear Non-Gaussian Polytree Models [2.4493299476776778]
  We propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges.
  arXiv  Detail & Related papers  (2022-08-13T18:20:10Z)
• Distributed Learning of Generalized Linear Causal Networks [19.381934612280993]
  We propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) DARLS is the first method for learning causal graphs with such theoretical guarantees. In a real-world application for modeling protein-DNA binding networks with distributed ChIP-Sequencing data, DARLS exhibits higher predictive power than other methods.
  arXiv  Detail & Related papers  (2022-01-23T06:33:25Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Learning Linear Polytree Structural Equation Models [4.833417613564028]
  We are interested in the problem of learning the directed acyclic graph (DAG) when data are generated from a linear structural equation model (SEM) We study sufficient conditions on the sample sizes for the well-known Chow-Liu algorithm to exactly recover both the skeleton and the equivalence class of the polytree. We also consider an extension of group linear polytree models, in which each node represents a group of variables.
  arXiv  Detail & Related papers  (2021-07-22T23:22:20Z)
• GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning [54.291331971813364]
  offline reinforcement learning approaches can be divided into proximal and uncertainty-aware methods. In this work, we demonstrate the benefit of combining the two in a latent variational model. Our proposed metrics measure both the quality of out of distribution samples as well as the discrepancy of examples in the data.
  arXiv  Detail & Related papers  (2021-02-22T19:42:40Z)
• Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
  We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
  arXiv  Detail & Related papers  (2020-07-13T03:27:45Z)

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.