Unifying Causal Representation Learning with the Invariance Principle

• URL: http://arxiv.org/abs/2409.02772v1
• Date: Wed, 4 Sep 2024 14:51:36 GMT
• Title: Unifying Causal Representation Learning with the Invariance Principle
• Authors: Dingling Yao, Dario Rancati, Riccardo Cadei, Marco Fumero, Francesco Locatello,
• Abstract summary: Causal representation learning aims at recovering latent causal variables from high-dimensional observations. Our main contribution is to show that many existing causal representation learning approaches methodologically align the representation to known data symmetries.
• Score: 21.375611599649716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification. A plethora of methods have been developed, each tackling carefully crafted problem settings that lead to different types of identifiability. The folklore is that these different settings are important, as they are often linked to different rungs of Pearl's causal hierarchy, although not all neatly fit. Our main contribution is to show that many existing causal representation learning approaches methodologically align the representation to known data symmetries. Identification of the variables is guided by equivalence classes across different data pockets that are not necessarily causal. This result suggests important implications, allowing us to unify many existing approaches in a single method that can mix and match different assumptions, including non-causal ones, based on the invariances relevant to our application. It also significantly benefits applicability, which we demonstrate by improving treatment effect estimation on real-world high-dimensional ecological data. Overall, this paper clarifies the role of causality assumptions in the discovery of causal variables and shifts the focus to preserving data symmetries.

Related papers

• Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures [49.1574468325115]
  Causal Discovery is a technique that tackles the challenge by analyzing the statistical properties of the constituent variables. We question the current (possibly misleading) baseline results on the basis that they were obtained through supervised learning. In consequence, we approach this problem in an unsupervised way, using robust Mutual Information measures.
  arXiv  Detail & Related papers  (2024-08-01T09:11:08Z)
• A Sparsity Principle for Partially Observable Causal Representation Learning [28.25303444099773]
  Causal representation learning aims at identifying high-level causal variables from perceptual data. We focus on learning from unpaired observations from a dataset with an instance-dependent partial observability pattern. We propose two methods for estimating the underlying causal variables by enforcing sparsity in the inferred representation.
  arXiv  Detail & Related papers  (2024-03-13T08:40:49Z)
• Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions [14.586959818386765]
  We provide the first identifiability result for causal representation learning that allows for multiple variables to be targeted by an intervention within one environment. Our approach hinges on a general assumption on the coverage and diversity of interventions across environments. In addition to and inspired by our theoretical contributions, we present a practical algorithm to learn causal representations from multi-node interventional data.
  arXiv  Detail & Related papers  (2023-11-05T16:05:00Z)
• 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)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• 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)
• 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)
• On Disentangled Representations Learned From Correlated Data [59.41587388303554]
  We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data. We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
  arXiv  Detail & Related papers  (2020-06-14T12:47:34Z)

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.