Structural restrictions in local causal discovery: identifying direct causes of a target variable

• URL: http://arxiv.org/abs/2307.16048v2
• Date: Mon, 29 Jul 2024 15:05:43 GMT
• Title: Structural restrictions in local causal discovery: identifying direct causes of a target variable
• Authors: Juraj Bodik, Valérie Chavez-Demoulin,
• Abstract summary: Learning a set of direct causes of a target variable from an observational joint distribution is a fundamental problem in science. Here, we are only interested in identifying the direct causes of one target variable, not the full DAG. This allows us to relax the identifiability assumptions and develop possibly faster and more robust algorithms.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs (DAGs) that represent the causal structure is a fundamental problem in science. Several results are known when the full DAG is identifiable from the distribution, such as assuming a nonlinear Gaussian data-generating process. Here, we are only interested in identifying the direct causes of one target variable (local causal structure), not the full DAG. This allows us to relax the identifiability assumptions and develop possibly faster and more robust algorithms. In contrast to the Invariance Causal Prediction framework, we only assume that we observe one environment without any interventions. We discuss different assumptions for the data-generating process of the target variable under which the set of direct causes is identifiable from the distribution. While doing so, we put essentially no assumptions on the variables other than the target variable. In addition to the novel identifiability results, we provide two practical algorithms for estimating the direct causes from a finite random sample and demonstrate their effectiveness on several benchmark and real datasets.

Related papers

• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding [1.6932009464531739]
  We consider linear non-Gaussian structural equation models that involve latent confounding. In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects.
  arXiv  Detail & Related papers  (2024-08-09T07:24:12Z)
• 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)
• Differentiable Invariant Causal Discovery [106.87950048845308]
  Learning causal structure from observational data is a fundamental challenge in machine learning. This paper proposes Differentiable Invariant Causal Discovery (DICD) to avoid learning spurious edges and wrong causal directions. Extensive experiments on synthetic and real-world datasets verify that DICD outperforms state-of-the-art causal discovery methods up to 36% in SHD.
  arXiv  Detail & Related papers  (2022-05-31T09:29:07Z)
• Typing assumptions improve identification in causal discovery [123.06886784834471]
  Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. We propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables.
  arXiv  Detail & Related papers  (2021-07-22T14:23:08Z)
• Variational Causal Networks: Approximate Bayesian Inference over Causal Structures [132.74509389517203]
  We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs. In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
  arXiv  Detail & Related papers  (2021-06-14T17:52:49Z)
• A Local Method for Identifying Causal Relations under Markov Equivalence [7.904790547594697]
  Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of a given target based on causal graphical models of directed acyclic graphs (DAGs)
  arXiv  Detail & Related papers  (2021-02-25T05:01:44Z)
• 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)
• Inferring Causal Direction from Observational Data: A Complexity Approach [0.3553493344868413]
  We propose several criteria for distinguishing cause and effect in pairs of discrete or continuous random variables. We demonstrate the accuracy of the criteria on synthetic data generated under a broad family of causal mechanisms and types of noise.
  arXiv  Detail & Related papers  (2020-10-12T12:10:37Z)
• Information-Theoretic Approximation to Causal Models [0.0]
  We show that it is possible to solve the problem of inferring the causal direction and causal effect between two random variables from a finite sample. We embed distributions that originate from samples of X and Y into a higher dimensional probability space. We show that this information-theoretic approximation to causal models (IACM) can be done by solving a linear optimization problem.
  arXiv  Detail & Related papers  (2020-07-29T18:34:58Z)

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.