arXiv:2603. 16798v2 Announce Type: replace Abstract: We study mean estimation for a Gaussian distribution with identity covariance in $\mathbb{R}^d$ under a missing data scheme termed realizable $\epsilon$-contamination model.
arXiv:2602. 16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions.
arXiv:2609.24086v1 Announce Type: cross Abstract: Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population infere...
The paper introduces an assumption‑lean framework that uses AI‑generated measurements as weak shadow variables to identify and infer population quantities when data are missing not at random. Weak shadow variables are outcome‑informative proxies that are conditionally independent of missingness given the true outcome and covariates, and they do not need to predict missing outcomes accurately. The authors derive sharp bounds via linear programs and propose a localized penalized estimator with a subsampling algorithm for confidence intervals, demonstrating in semi‑synthetic experiments that the resulting intervals are substantially narrower and more accurate than classical MNAR methods.
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
The paper addresses the problem of covariate mismatch in collaborative linear prediction, where different centers collect different sets of features. It proposes estimators that leverage information across centers under center-wise MCAR missingness, including a plug‑in estimator for low dimensions and an impute‑then‑regress strategy with ridge regularization for higher dimensions. All methods comply with federated learning constraints, exchanging only aggregated statistics, and the authors provide theoretical learning rates and numerical validation.
arXiv:2509. 19242v2 Announce Type: replace-cross Abstract: We study multivariate linear regression under Gaussian covariates in two settings, where data may be erased or corrupted by an adversary under a coordinate-wise budget.
arXiv:2605.04469v2 Announce Type: replace-cross 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...
arXiv:2607. 07767v1 Announce Type: cross Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution.
arXiv:2609.37664v1 Announce Type: new Abstract: Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training dat...
arXiv:2504. 15388v3 Announce Type: replace-cross Abstract: In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied in conjunction with any existing imputation technique.
The paper introduces a Bayesian approach to matrix completion that uses a nuclear norm-based prior and addresses the challenge of unknown noise variance by placing a prior on it. It presents the first sampler for this model, providing a non‑asymptotic polynomial‑time guarantee in terms of matrix dimensions and desired accuracy. The method discretizes the noise precision and employs thermodynamic integration to construct a categorical posterior, offering a feasibility result for Bayesian sampling in non‑log‑concave settings.