SCADI: Self-supervised Causal Disentanglement in Latent Variable Models

• URL: http://arxiv.org/abs/2311.06567v1
• Date: Sat, 11 Nov 2023 13:33:43 GMT
• Title: SCADI: Self-supervised Causal Disentanglement in Latent Variable Models
• Authors: Heejeong Nam
• Abstract summary: We propose a novel model, SCADI(SElf-supervised CAusal DIsentanglement), that enables the model to discover semantic factors and learn their causal relationships without supervision. This model combines a masked structural causal model (SCM) with a pseudo-label generator for causal disentanglement, aiming to provide a new direction for self-supervised causal disentanglement models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal disentanglement has great potential for capturing complex situations. However, there is a lack of practical and efficient approaches. It is already known that most unsupervised disentangling methods are unable to produce identifiable results without additional information, often leading to randomly disentangled output. Therefore, most existing models for disentangling are weakly supervised, providing information about intrinsic factors, which incurs excessive costs. Therefore, we propose a novel model, SCADI(SElf-supervised CAusal DIsentanglement), that enables the model to discover semantic factors and learn their causal relationships without any supervision. This model combines a masked structural causal model (SCM) with a pseudo-label generator for causal disentanglement, aiming to provide a new direction for self-supervised causal disentanglement models.

Related papers

• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows [6.954510776782872]
  Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling causal relationships. We introduce CAF-PoNo, harnessing the power of the normalizing flows architecture to enforce the crucial invertibility constraint in PNL models. Our method precisely reconstructs the hidden noise, which plays a vital role in cause-effect identification.
  arXiv  Detail & Related papers  (2024-07-06T07:19:21Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation [50.93536377097659]
  This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors.
  arXiv  Detail & Related papers  (2023-04-17T00:10:56Z)
• De-Biasing Generative Models using Counterfactual Methods [0.0]
  We propose a new decoder based framework named the Causal Counterfactual Generative Model (CCGM) Our proposed method combines a causal latent space VAE model with specific modification to emphasize causal fidelity. We explore how better disentanglement of causal learning and encoding/decoding generates higher causal intervention quality.
  arXiv  Detail & Related papers  (2022-07-04T16:53:20Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• Restricted Hidden Cardinality Constraints in Causal Models [0.0]
  Causal models with unobserved variables impose nontrivial constraints on the distributions over the observed variables. We consider causal models with a promise that unobserved variables have known cardinalities.
  arXiv  Detail & Related papers  (2021-09-13T00:52:08Z)
• 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)
• 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)
• Weakly Supervised Disentangled Generative Causal Representation Learning [21.392372783459013]
  We show that previous methods with independent priors fail to disentangle causally related factors even under supervision. We propose a new disentangled learning method that enables causal controllable generation and causal representation learning.
  arXiv  Detail & Related papers  (2020-10-06T11:38:41Z)

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.