Identifying Structural Biases from Causal Mechanism Shifts

• URL: http://arxiv.org/abs/2606.18834v1
• Date: Wed, 17 Jun 2026 09:09:32 GMT
• Title: Identifying Structural Biases from Causal Mechanism Shifts
• Abstract summary: We show how to identify hidden confounding and selection biases from causal mechanism shifts.<n>In particular, we show that structural biases lead to dependent mechanism shifts.<n>To tell which nodes are subject to what kind of bias, we introduce the StruBI algorithm.
• Score: 34.67784777106841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In practice, these assumptions are often violated, leading to inaccurate inference. In this paper, we study how to identify hidden confounding and selection biases from causal mechanism shifts. In particular, we show that structural biases lead to dependent mechanism shifts. That is, by considering for which variables the mechanisms change given data from different environments, we can tell which variables are unbiased, which are subject to hidden confounding, and which are undergoing selection bias. We formalize this into an empirically testable criterion based on mutual information, and show under which conditions it identifies structural biases. To tell which nodes are subject to what kind of bias, we introduce the StruBI algorithm. Experiments on synthetic and real-world data show that StruBI works well in practice, accurately recovering affected variable sets and types of biases, outperforming the state-of-the-art by a wide margin.

Related papers

• Latent Variable Causal Discovery under Selection Bias [31.127374830119454]
  We study rank constraints, which as a generalization to conditional independence constraints, exploits the ranks of covariance submatrices in linear Gaussian models.<n>We show that although selection can significantly complicate the joint distribution, the ranks in the biased covariance matrices still preserve meaningful information about both causal structures and selection mechanisms.<n>Using this tool, we demonstrate that the one-factor model, a classical latent variable model, can be identified under selection bias.
  arXiv  Detail & Related papers  (2025-12-12T02:00:01Z)
• Detecting and Identifying Selection Structure in Sequential Data [53.24493902162797]
  We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. We show that selection structure is identifiable without any parametric assumptions or interventional experiments. We also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies.
  arXiv  Detail & Related papers  (2024-06-29T20:56:34Z)
• iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models [48.33685559041322]
  This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables. Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN.
  arXiv  Detail & Related papers  (2023-06-30T01:48:11Z)
• 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)
• Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis [7.895866278697778]
  Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, this assumption is almost always violated due to distribution shifts between environments. We propose the Mechanism Shift Score (MSS), a score-based approach amenable to various empirical estimators.
  arXiv  Detail & Related papers  (2022-06-04T15:39:30Z)
• Causal Discovery in Linear Structural Causal Models with Deterministic Relations [27.06618125828978]
  We focus on the task of causal discovery form observational data. We derive a set of necessary and sufficient conditions for unique identifiability of the causal structure.
  arXiv  Detail & Related papers  (2021-10-30T21:32:42Z)
• Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests [87.60900567941428]
  A spurious correlation' is the dependence of a model on some aspect of the input data that an analyst thinks shouldn't matter. In machine learning, these have a know-it-when-you-see-it character. We study stress testing using the tools of causal inference.
  arXiv  Detail & Related papers  (2021-05-31T14:39:38Z)
• Stable Prediction via Leveraging Seed Variable [73.9770220107874]
  Previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. We propose a conditional independence test based algorithm to separate causal variables with a seed variable as priori, and adopt them for stable prediction. Our algorithm outperforms state-of-the-art methods for stable prediction.
  arXiv  Detail & Related papers  (2020-06-09T06:56:31Z)
• CausalVAE: Structured Causal Disentanglement in Variational Autoencoder [52.139696854386976]
  The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. We propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent factors into causal endogenous ones. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy.
  arXiv  Detail & Related papers  (2020-04-18T20:09:34Z)

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.