Augmented transfer regression learning for completely missing covariates

• URL: http://arxiv.org/abs/2605.04469v1
• Date: Wed, 06 May 2026 03:48:28 GMT
• Title: Augmented transfer regression learning for completely missing covariates
• Abstract summary: Large-scale population-level datasets often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them.<n>This creates a cross-population missing data problem.<n>We propose an augmented transfer regression learning method for this setting.
• Score: 0.8603348174612194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them. This creates a cross-population missing data problem in which covariates are completely unobserved in the target population, rather than partially missing within one dataset. We propose an augmented transfer regression learning method for this setting. The key identifying condition is a sub-population shift assumption: the joint distribution of the outcome and observed covariates may differ across source and target populations, but the conditional distribution of the missing covariates given observed variables is invariant. We combine importance-weighted estimating equations with imputation terms for first- and second-order moments of the missing covariates. The resulting estimator is doubly robust, remaining consistent if either the density ratio model or both imputation models are correctly specified. It is $n^{1/2}$-consistent and asymptotically normal, and attains the semiparametric efficiency bound when both nuisance models are correctly specified.

Related papers

• Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation [71.74203645336385]
  Impute-EM alternates between imputing missing entries with the current model and refitting a diffusion backbone on completed data.<n>We characterize the update and show that the observed mask-indexed marginals match the targets at the limit.
  arXiv  Detail & Related papers  (2026-09-14T09:39:13Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression [102.24287051757469]
  We study self-supervised covariance estimation in deep heteroscedastic regression.<n>We derive an upper bound on the 2-Wasserstein distance between normal distributions.<n>Experiments over a wide range of synthetic and real datasets demonstrate that the proposed 2-Wasserstein bound coupled with pseudo label annotations results in a computationally cheaper yet accurate deep heteroscedastic regression.
  arXiv  Detail & Related papers  (2025-02-14T22:37:11Z)
• Accounting for Missing Covariates in Heterogeneous Treatment Estimation [17.09751619857397]
  We introduce a novel partial identification strategy based on ideas from ecological inference. We show that our framework can produce bounds that are much tighter than would otherwise be possible.
  arXiv  Detail & Related papers  (2024-10-21T05:47:07Z)
• High-dimensional logistic regression with missing data: Imputation, regularization, and universality [7.167672851569787]
  We study high-dimensional, ridge-regularized logistic regression. We provide exact characterizations of both the prediction error and the estimation error.
  arXiv  Detail & Related papers  (2024-10-01T21:41:21Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression [109.69084997173196]
  Deepscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. Recent works show that this may result in sub-optimal convergence due to the challenges associated with covariance estimation. We study two questions: (1) Does the predicted covariance truly capture the randomness of the predicted mean? Our results show that not only does TIC accurately learn the covariance, it additionally facilitates an improved convergence of the negative log-likelihood.
  arXiv  Detail & Related papers  (2023-10-29T09:54:03Z)
• Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation [7.8856737627153874]
  We propose a doubly robust estimator for covariate shift adaptation via importance weighting. Our estimator reduces the bias arising from the density ratio estimation errors. Notably, our estimator remains consistent if either the density ratio estimator or the regression function is consistent.
  arXiv  Detail & Related papers  (2023-10-25T13:38:29Z)
• Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects [1.9253333342733674]
  Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. We propose a unified and model-agnostic inferential approach for a wide class of partially identified estimands.
  arXiv  Detail & Related papers  (2023-10-12T08:17:30Z)
• 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)
• Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression [2.520538806201793]
  In traditional logistic regression models, the link function is often assumed to be linear and continuous in predictors. We consider a threshold model that all continuous features are discretized into ordinal levels, which further determine the binary responses. We find the lasso model is well suited in the problem of early detection and prediction for chronic disease like diabetes.
  arXiv  Detail & Related papers  (2022-02-17T04:16:40Z)

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.