Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms

• URL: http://arxiv.org/abs/2401.18017v1
• Date: Wed, 31 Jan 2024 17:28:05 GMT
• Title: Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms
• Authors: Tim Tse, Zhitang Chen, Shengyu Zhu, Yue Liu
• Abstract summary: We propose a novel score measure based on heterogeneous transformation of RKHS embeddings to extract relevant higher-order moments of the conditional densities for causal discovery. Inference is made via comparing the score of each hypothetical cause-effect direction.
• Score: 17.368146833023893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discovery of causal relationships in a set of random variables is a fundamental objective of science and has also recently been argued as being an essential component towards real machine intelligence. One class of causal discovery techniques are founded based on the argument that there are inherent structural asymmetries between the causal and anti-causal direction which could be leveraged in determining the direction of causation. To go about capturing these discrepancies between cause and effect remains to be a challenge and many current state-of-the-art algorithms propose to compare the norms of the kernel mean embeddings of the conditional distributions. In this work, we argue that such approaches based on RKHS embeddings are insufficient in capturing principal markers of cause-effect asymmetry involving higher-order structural variabilities of the conditional distributions. We propose Kernel Intrinsic Invariance Measure with Heterogeneous Transform (KIIM-HT) which introduces a novel score measure based on heterogeneous transformation of RKHS embeddings to extract relevant higher-order moments of the conditional densities for causal discovery. Inference is made via comparing the score of each hypothetical cause-effect direction. Tests and comparisons on a synthetic dataset, a two-dimensional synthetic dataset and the real-world benchmark dataset T\"ubingen Cause-Effect Pairs verify our approach. In addition, we conduct a sensitivity analysis to the regularization parameter to faithfully compare previous work to our method and an experiment with trials on varied hyperparameter values to showcase the robustness of our algorithm.

Related papers

• A General Causal Inference Framework for Cross-Sectional Observational Data [0.4972323953932129]
  General Causal Inference (GCI) framework specifically designed for cross-sectional observational data. This paper proposes a GCI framework specifically designed for cross-sectional observational data.
  arXiv  Detail & Related papers  (2024-04-28T14:26:27Z)
• Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants [31.85295338809117]
  We propose a novel approach to identify the existence of a causal edge between two observed variables subject to latent variable influence. In case when such a causal edge exits, we introduce an asymmetry criterion to determine the causal direction.
  arXiv  Detail & Related papers  (2023-12-19T08:20:19Z)
• Towards stable real-world equation discovery with assessing differentiating quality influence [52.2980614912553]
  We propose alternatives to the commonly used finite differences-based method. We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
  arXiv  Detail & Related papers  (2023-11-09T23:32:06Z)
• 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)
• Variation-based Cause Effect Identification [5.744133015573047]
  We propose a variation-based cause effect identification (VCEI) framework for causal discovery. Our framework relies on the principle of independence of cause and mechanism (ICM) under the assumption of an existing acyclic causal link. In the causal direction, such variations are expected to have no impact on the effect generation mechanism.
  arXiv  Detail & Related papers  (2022-11-22T05:19:12Z)
• 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)
• 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)
• 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)
• Causal Inference in Geoscience and Remote Sensing from Observational Data [9.800027003240674]
  We try to estimate the correct direction of causation using a finite set of empirical data. We illustrate performance in a collection of 28 geoscience causal inference problems. The criterion achieves state-of-the-art detection rates in all cases, it is generally robust to noise sources and distortions.
  arXiv  Detail & Related papers  (2020-12-07T22:56:55Z)
• 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)
• Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders [6.1221613913018675]
  This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders.
  arXiv  Detail & Related papers  (2020-01-13T12:55:47Z)

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.