Emputation is a deep generative imputation framework whose energy-score training objective is explicitly guided by pattern-mixture identification assumptions, with proofs that the population minimizer recovers the target extrapolation distribution.
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7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
StruMPL is a multi-task dense regression model that jointly addresses disjoint partial supervision, MNAR labels, and inter-task physical constraints for improved forest biomass estimation from Earth observation.
A new bootstrap goodness-of-fit test for the logistic propensity score model under nonignorable missing data, based on marginal sum-of-squared residuals, with asymptotic size and power guarantees.
MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
The paper defines possibility space, timing computation, and causal factum to make timing a computable variable, and illustrates the framework with automatic trajectory discovery and counterfactual timing on 3,276 breast-cancer patients.
citing papers explorer
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Emputation: Identification-Guided Neural Imputation Framework
Emputation is a deep generative imputation framework whose energy-score training objective is explicitly guided by pattern-mixture identification assumptions, with proofs that the population minimizer recovers the target extrapolation distribution.
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Causal Inference for All: Marginal Estimands for Outcomes Truncated by Death
Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
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StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels
StruMPL is a multi-task dense regression model that jointly addresses disjoint partial supervision, MNAR labels, and inter-task physical constraints for improved forest biomass estimation from Earth observation.
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A goodness-of-fit test for the logistic propensity score model under nonignorable missing data
A new bootstrap goodness-of-fit test for the logistic propensity score model under nonignorable missing data, based on marginal sum-of-squared residuals, with asymptotic size and power guarantees.
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Missingness-Adaptive Factor Identification in High-Dimensional Data
MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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To Use AI as Dice of Possibilities with Timing Computation
The paper defines possibility space, timing computation, and causal factum to make timing a computable variable, and illustrates the framework with automatic trajectory discovery and counterfactual timing on 3,276 breast-cancer patients.