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arXiv:2607. 14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints.
arXiv:2211. 14297v4 Announce Type: replace-cross Abstract: We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models.
arXiv:2411.12965v3 Announce Type: replace-cross Abstract: Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making s...
arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.
arXiv:2605. 17189v2 Announce Type: replace-cross Abstract: Inductive matrix completion (IMC) is a variant of low-rank matrix completion that incorporates row and column side-information.
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.