Controlling for discrete unmeasured confounding in nonlinear causal models

• URL: http://arxiv.org/abs/2408.05647v1
• Date: Sat, 10 Aug 2024 22:50:08 GMT
• Title: Controlling for discrete unmeasured confounding in nonlinear causal models
• Authors: Patrick Burauel, Frederick Eberhardt, Michel Besserve,
• Abstract summary: We show theoretically that confounding can be detected and corrected under the assumption that the observed data is a piecewise affine transformation of a latent Gaussian mixture model. We provide a flow-based algorithm to estimate this model and perform deconfounding.
• Score: 6.255285109437089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmeasured confounding is a major challenge for identifying causal relationships from non-experimental data. Here, we propose a method that can accommodate unmeasured discrete confounding. Extending recent identifiability results in deep latent variable models, we show theoretically that confounding can be detected and corrected under the assumption that the observed data is a piecewise affine transformation of a latent Gaussian mixture model and that the identity of the mixture components is confounded. We provide a flow-based algorithm to estimate this model and perform deconfounding. Experimental results on synthetic and real-world data provide support for the effectiveness of our approach.

Related papers

• Assumption-Lean Post-Integrated Inference with Negative Control Outcomes [0.0]
  We introduce a robust post-integrated inference (PII) method that adjusts for latent heterogeneity using negative control outcomes. Our method extends to projected direct effect estimands, accounting for hidden mediators, confounders, and moderators. The proposed doubly robust estimators are consistent and efficient under minimal assumptions and potential misspecification.
  arXiv  Detail & Related papers  (2024-10-07T12:52:38Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• 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)
• Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach [18.007258270845107]
  We propose LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions. We apply our method to real-world image denoising and super-resolution tasks and train the models using the synthetic data generated by the LUD-VAE.
  arXiv  Detail & Related papers  (2022-04-21T13:27:17Z)
• 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)
• A Subsampling-Based Method for Causal Discovery on Discrete Data [18.35147325731821]
  In this work, we propose a subsampling-based method to test the independence between the generating schemes of the cause and that of the mechanism. Our methodology works for both discrete and categorical data and does not imply any functional model on the data, making it a more flexible approach.
  arXiv  Detail & Related papers  (2021-08-31T17:11:58Z)
• Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics [85.31247588089686]
  We show that variational Bayesian methods can yield sensitivities with respect to parametric and nonparametric aspects of Bayesian models. We provide both theoretical and empirical support for our variational approach to Bayesian sensitivity analysis.
  arXiv  Detail & Related papers  (2021-07-08T03:40:18Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• 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)
• Learning Stochastic Behaviour from Aggregate Data [52.012857267317784]
  Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available. We propose a novel method using the weak form of Fokker Planck Equation (FPE) to describe the density evolution of data in a sampled form. In such a sample-based framework we are able to learn the nonlinear dynamics from aggregate data without explicitly solving the partial differential equation (PDE) FPE.
  arXiv  Detail & Related papers  (2020-02-10T03:20:13Z)

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.