Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method

• URL: http://arxiv.org/abs/2001.01894v1
• Date: Tue, 7 Jan 2020 05:16:30 GMT
• Title: Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
• Authors: Pengzhou Wu and Kenji Fukumizu
• Abstract summary: We train nonparametrically general nonlinear causal models that allow non-additive noise. We build an ensemble framework, namely Causal Mosaic, which models a causal pair by a mixture of nonlinear models.
• Score: 18.44408086531395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of distinguishing cause from effect in bivariate setting. Based on recent developments in nonlinear independent component analysis (ICA), we train nonparametrically general nonlinear causal models that allow non-additive noise. Further, we build an ensemble framework, namely Causal Mosaic, which models a causal pair by a mixture of nonlinear models. We compare this method with other recent methods on artificial and real world benchmark datasets, and our method shows state-of-the-art performance.

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)
• Assessing the overall and partial causal well-specification of nonlinear additive noise models [4.13592995550836]
  We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecifications. We propose an algorithm for finite sample data, discuss its properties, and illustrate its performance on simulated and real data.
  arXiv  Detail & Related papers  (2023-10-25T09:44:16Z)
• Discovering Mixtures of Structural Causal Models from Time Series Data [23.18511951330646]
  We propose a general variational inference-based framework called MCD to infer the underlying causal models. Our approach employs an end-to-end training process that maximizes an evidence-lower bound for the data likelihood. We demonstrate that our method surpasses state-of-the-art benchmarks in causal discovery tasks.
  arXiv  Detail & Related papers  (2023-10-10T05:13:10Z)
• 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)
• Hessian Eigenspectra of More Realistic Nonlinear Models [73.31363313577941]
  We make a emphprecise characterization of the Hessian eigenspectra for a broad family of nonlinear models. Our analysis takes a step forward to identify the origin of many striking features observed in more complex machine learning models.
  arXiv  Detail & Related papers  (2021-03-02T06:59:52Z)
• Nonlinear Independent Component Analysis for Continuous-Time Signals [85.59763606620938]
  We study the classical problem of recovering a multidimensional source process from observations of mixtures of this process. We show that this recovery is possible for many popular models of processes (up to order and monotone scaling of their coordinates) if the mixture is given by a sufficiently differentiable, invertible function.
  arXiv  Detail & Related papers  (2021-02-04T20:28:44Z)
• Causal Inference Using Linear Time-Varying Filters with Additive Noise [18.35147325731821]
  Causal inference using the restricted structural causal model framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms. We propose to break the symmetry by exploiting the nonstationarity of the data. Our main theoretical result shows that the causal direction is identifiable in generic cases when cause and effect are connected via a time-varying filter.
  arXiv  Detail & Related papers  (2020-12-23T23:35:58Z)
• Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis [4.7210697296108926]
  We propose a new estimation method for a linear DAG model with non-Gaussian noises. The proposed method enables us to estimate the causal order and the parameters simultaneously. Numerical experiments show that the proposed method outperforms existing methods.
  arXiv  Detail & Related papers  (2020-09-07T13:08:10Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)
• 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.