Causal Discovery in Linear Models with Unobserved Variables and Measurement Error

• URL: http://arxiv.org/abs/2407.19426v1
• Date: Sun, 28 Jul 2024 08:26:56 GMT
• Title: Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
• Authors: Yuqin Yang, Mohamed Nafea, Negar Kiyavash, Kun Zhang, AmirEmad Ghassami,
• Abstract summary: The presence of unobserved common causes and the presence of measurement error are two of the most limiting challenges in the task of causal structure learning. We study the problem of causal discovery in systems where these two challenges can be present simultaneously.
• Score: 26.72594853233639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The presence of unobserved common causes and the presence of measurement error are two of the most limiting challenges in the task of causal structure learning. Ignoring either of the two challenges can lead to detecting spurious causal links among variables of interest. In this paper, we study the problem of causal discovery in systems where these two challenges can be present simultaneously. We consider linear models which include four types of variables: variables that are directly observed, variables that are not directly observed but are measured with error, the corresponding measurements, and variables that are neither observed nor measured. We characterize the extent of identifiability of such model under separability condition (i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables is identifiable) together with two versions of faithfulness assumptions and propose a notion of observational equivalence. We provide graphical characterization of the models that are equivalent and present a recovery algorithm that could return models equivalent to the ground truth.

Related papers

• Score matching through the roof: linear, nonlinear, and latent variables causal discovery [18.46845413928147]
  Causal discovery from observational data holds great promise. Existing methods rely on strong assumptions about the underlying causal structure. We propose a flexible algorithm for causal discovery across linear, nonlinear, and latent variable models.
  arXiv  Detail & Related papers  (2024-07-26T14:09:06Z)
• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning [0.8158530638728501]
  Causal Discovery aims to find automated search methods for learning causal structures from observational data. This thesis focuses on two questions in causal discovery: (i) providing an alternative definition of k-Triangle Faithfulness that (i) is weaker than strong faithfulness when applied to the Gaussian family of distributions, and (ii) under the assumption that the modified version of Strong Faithfulness holds.
  arXiv  Detail & Related papers  (2023-08-15T01:23:42Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Causal Discovery in Linear Latent Variable Models Subject to Measurement Error [29.78435955758185]
  We focus on causal discovery in the presence of measurement error in linear systems. We demonstrate a surprising connection between this problem and causal discovery in the presence of unobserved parentless causes.
  arXiv  Detail & Related papers  (2022-11-08T03:43:14Z)
• Identifying Weight-Variant Latent Causal Models [82.14087963690561]
  We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. We propose a novel method, termed Structural caUsAl Variational autoEncoder, which directly learns latent causal representations and causal relationships among them.
  arXiv  Detail & Related papers  (2022-08-30T11:12:59Z)
• Discovery of Causal Additive Models in the Presence of Unobserved Variables [6.670414650224422]
  Causal discovery from data affected by unobserved variables is an important but difficult problem to solve. We propose a method to identify all the causal relationships that are theoretically possible to identify without being biased by unobserved variables.
  arXiv  Detail & Related papers  (2021-06-04T03:28:27Z)
• Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding [101.35070661471124]
  We show that unobserved confounding leaves a characteristic footprint in the observed data distribution that allows for disentangling spurious and causal effects. We propose an adjusted score-based causal discovery algorithm that may be implemented with general-purpose solvers and scales to high-dimensional problems.
  arXiv  Detail & Related papers  (2021-03-28T11:07:59Z)
• 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)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)

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.