New algorithms handle missing outcomes in MAB, reducing regret.
problem Missing outcomes in real-world MAB scenarios lead to biased estimates and linear regret.
method Introduced algorithms for MAR and MNAR missingness mechanisms in MAB.
result Significant improvements in decision-making by accounting for missingness.
Optimizes data labeling for causal effect estimation with missing outcomes.
problem Estimating causal effects with missing outcome data and budget constraints.
method Optimizes batch sampling probability to minimize variance of causal inference estimator.
result Achieves lower mean-squared error with fewer labeled data points.
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
Bounds and sensitivity analysis for causal effects with MNAR confounders.
problem Estimating causal effects with missing outcome data.
method Assumption-free bounds and sensitivity analysis for outcome-independent MNAR.
result Valid bounds and sensitivity analysis methods for causal effect estimation.
New methods for estimating treatment effects with missing data.
problem Missing outcome data complicates estimating treatment effects.
method Proposed two de-biased machine learning estimators (mDR-learner and mEP-learner) to address under-representation.
result Oracle efficiency of the proposed estimators under reasonable conditions.
Study tackles variable selection with missing covariates and outcomes using machine learning and imputation.
problem Missing data in both covariates and outcomes complicates variable selection in health studies.
method Exploits machine learning flexibility and bootstrap imputation for variable selection, comparing multiple methods.
result XGBoost and BART perform best in variable selection with bootstrap imputation, achieving high F1 scores and low Type I errors. A new Thompson Sampling framework handles uncertainty by imputing missing data.
problem Handling uncertainty in contextual bandit problems.
method Generative model to impute missing outcomes, fit policy, and select actions.
result Established a state-of-the-art regret bound that depends on generative model quality.
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic …
Two-stage TMLE reduces bias and improves efficiency in CRTs.
problem Differential outcome measurement and imbalance in baseline predictors in CRTs.
method Two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates.
result Our approach nearly eliminates bias due to differential outcome measurement.
The paper proposes a method to estimate treatment effects using surrogates when primary outcomes are missing.
problem Missing primary outcomes in causal inference applications can lead to biased estimates.
method Doubly robust method that uses both labeled and unlabeled data, incorporating surrogates.
result The proposed estimator is asymptotically normal and has improved variance compared to methods using only labeled data.
TMLE improves causal effect estimation in missing data scenarios with various positivity violations.
problem Estimating causal effects in studies with missing data and positivity violations.
method Targeted Maximum Likelihood Estimation (TMLE) with various missing data methods.
result Complete cases with TMLE incorporating an outcome-missingness model exhibit lower bias and greater robustness against positivity violations.
A fundamental challenge in semi-supervised learning lies in the observed data's disproportional size when compared with the size of the data collected with missing outcomes. An implicit understanding is that the dataset with missing outcomes, being significantly larger, ought to improve estimation and inference. Howeve…
Estimates missing distributions using nearest neighbors with kernel methods.
problem Missing data and unobserved confounding in multivariate distributions.
method Distributional matrix completion framework with kernel nearest neighbors.
result Consistent recovery of underlying distributions with missing data.
Study uses surrogate data to improve treatment effect estimation with scarce outcome data.
problem Limited outcome data hinders estimating treatment effects.
method Uses abundant surrogate data to estimate treatment effects without stringent assumptions.
result Improves precision of treatment effect estimation.
Two methods use BART to model missing data in leaf photosynthetic trait data.
problem Handling missing data in multivariate outcomes with non-ignorable mechanisms.
method Bayesian Additive Regression Trees (BART) for joint modeling of data and missingness indicators.
result Both methods effectively recover various missingness mechanisms and outperform existing approaches.
Novel concentration inequalities are obtained for the missing mass, i.e. the total probability mass of the outcomes not observed in the sample. We derive distribution-free deviation bounds with sublinear exponents in deviation size for missing mass and improve the results of Berend and Kontorovich (2013) and Yari Saeed…
Kernel ridge regression for causal inference with missing data.
problem Estimating treatment effects with missing data in selected samples.
method Kernel ridge regression estimators for nonparametric dose response curves and semiparametric treatment effects.
result Uniform consistency and finite sample rates for continuous treatment, root-n consistency for discrete treatment.
Estimator improves prediction with missing data in multi-environment settings.
problem Handling missing data in multi-environment settings for robust prediction.
method Derive an estimator from invariance objective under missing outcomes.
result The estimator achieves lower prediction error despite using a biased imputation model.
Proposes a method to improve CATE estimation by imputing missing potential outcomes.
problem Statistical discrepancy between distinct treatment groups in CATE estimation.
method Contrastive learning approach to reliably impute missing potential outcomes for a subset of individuals.
result Improves the accuracy and robustness of CATE estimation models.
Study examines impact of missing data on multi-armed bandit algorithms.
problem Impact of missing data on performance of multi-armed bandit algorithms.
method Extensive simulation study of two-armed bandit algorithms with binary outcomes, considering different probabilities of missingness.
result Impact on performance varies depending on the balance between exploration and exploitation.
Unified ML imputation framework for missing data.
problem Invalid inference with incomplete data and ML imputations.
method Stratified by missingness patterns, weights and aggregates complete case information.
result Valid statistical inference for general missingness patterns.
Missing responses is a missing data format in which outcomes are not always observed. In this work we develop kernel machines that can handle missing responses. First, we propose a kernel machine family that uses mainly the complete cases. For the quadratic loss, we then propose a family of doubly-robust kernel machine…
New approach uses autoregressive models to explore and quantify uncertainty in decision-making.
problem Quantifying and exploring uncertainty in online decision-making.
method Reformulates uncertainty as missing future outcomes, training autoregressive models for next-outcome prediction.
result Establishes a reduction from online learning to offline next-outcome prediction, controlling Bayesian regret by sequence prediction loss.
In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorit…
DRUM transfers cardiac arrest models across registries with missing covariates.
problem Clinical prediction models fail when key training covariates are unavailable at deployment.
method DRUM transfers models to target populations with structurally missing covariates, optimizing worst-case predictive performance over unknown target distributions.
result DRUM yields better-calibrated predictions and improved clinical classification performance across sites.
HPPCA improves imputation of longitudinal data with missing values.
problem Handling incomplete, high-dimensional longitudinal data with nested sources of variation and temporal dependency.
method Hierarchical probabilistic principal component analysis (HPPCA) with a two-level latent factor model and Gaussian process.
result HPPCA outperforms standard PPCA and multivariate functional PCA in imputation accuracy, even under heavy missingness and model misspecification.
The paper addresses estimating long-term treatment effects with monotone missing data.
problem Estimating long-term treatment effects with missing data, especially monotone missing.
method The paper introduces the sequential missingness assumption for identification and proposes three novel estimation methods: inverse probability weighting, sequential regression imputation, and SeqMSM. It also introduces a balancing-enhanced approach, BalanceNet, to improve estimation accuracy.
result The proposed methods, including BalanceNet, effectively estimate long-term treatment effects with monotone missing data.
ELMV uses ensemble learning to handle missing values in EHR data.
problem Significant missing values in EHR data cause bias and unreliable conclusions.
method ELMV constructs multiple subsets with lower missing rates and uses a support set for ensemble learning.
result ELMV outperforms conventional methods in critical feature identification and outcome prediction.
This paper shows that one cannot learn the probability of rare events without imposing further structural assumptions. The event of interest is that of obtaining an outcome outside the coverage of an i.i.d. sample from a discrete distribution. The probability of this event is referred to as the "missing mass". The impo…
We tackle missing data in SBI methods and introduce a neural process approach.
problem Missing data in SBI methods can bias parameter estimation.
method We introduce a neural process approach to jointly learn imputation and inference.
result Our method provides robust inference outcomes compared to baselines.
We are concerned with obtaining novel concentration inequalities for the missing mass, i.e. the total probability mass of the outcomes not observed in the sample. We not only derive - for the first time - distribution-free Bernstein-like deviation bounds with sublinear exponents in deviation size for missing mass, but …
Method tackles missing covariates in large-scale datasets.
problem Cross-population missing data problem in large-scale datasets.
method Augmented transfer regression learning method combining importance-weighted estimating equations and imputation terms.
result Estimator is n1/2-consistent and asymptotically normal, attaining semiparametric efficiency bound under correct specification. New method for valid prediction intervals with coarsened data.
problem Handling missing data and censored outcomes in training samples.
method Multiply robust conformal risk control with semiparametric theory.
result Stronger coverage properties under covariate shift.
Imputation-free method learns tabular data with missing values using transformer.
problem Machine learning on tabular data with missing values often leads to unreliable outcomes due to synthetic imputation.
method Incremental attention learning (IFIAL) using transformer with attention masks.
result IFIAL outperforms state-of-the-art methods in 17 diverse tabular data sets.
A new method for handling missing values in data.
problem Handling missing values in machine learning models.
method Sharing pattern submodels with sparsity-inducing regularization.
result Sharing pattern submodels provide robust predictions and maintain/improve pattern submodel performance.
Estimates treatment effects in time series data with always-missing controls.
problem Lack of control group in time series data, especially during specific events.
method Recover control group in event period, account for confounders and temporal dependencies.
result Robust estimation of control group's potential outcome and accurate predicted holiday effect.
We consider high dimensional M-estimation in settings where the response Y is possibly missing at random and the covariates X∈Rp can be high dimensional compared to the sample size n. The parameter of interest θ0∈Rd is defined as the minimizer of the risk of a …
Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because s…
BSFP method reveals latent patterns in multi-omic data for predicting lung function in HIV-associated OLD.
problem Limited understanding of multi-omic molecular phenomena and clinical outcomes in obstructive lung disease.
method Bayesian Simultaneous Factorization and Prediction (BSFP) method for multi-omic data, accommodating imputation and full posterior inference.
result BSFP reveals distinct clusters of patients with OLD and multi-omic patterns related to lung function decline.
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
problem Statistical inference challenges with missing at random labels and spatial dependence.
method Doubly robust estimator with cross-fit nuisances and jackknife spatial HAC variance correction.
result Asymptotically valid confidence intervals with improved finite-sample calibration.
The paper targets optimal interventions for long-term outcomes using imputed data and policy learning.
problem Maximizing long-term outcomes observed only in the future.
method Imputing missing long-term outcomes and using a doubly-robust approach for policy evaluation and optimization.
result The approach outperforms simple short-term proxies and achieves significant revenue impact over three years.
FOCUS method forecasts counterfactuals in panel data with time series dynamics.
problem Forecasting unobserved potential outcomes in causal inference with missing entries and latent factors.
method FOCUS extends matrix completion methods by leveraging time series dynamics of latent factors.
result FOCUS method outperforms existing benchmarks in predicting future counterfactuals.
Bayesian approach models nonignorable missing data using copulas and marginal quantiles.
problem Nonignorable missing data in lead exposure and test score analysis.
method Gaussian copula model with auxiliary marginal quantiles for missingness indicators and study variables.
result Efficient MCMC algorithm estimates copula correlation and marginal distributions consistently.
New research shows imputation and regression together can predict better than separate steps.
problem Predicting with data missing values without strong assumptions.
method Proposes a joint imputation and regression approach using NeuMiss neural network.
result Joint imputation and regression outperforms separate imputation and regression methods.
Imputation for prediction often offers limited benefits, especially with powerful models.
problem The challenge of missing data in predictive models.
method Comparative analysis of imputation methods across various predictive models and datasets.
result Advanced imputation methods often offer limited benefits for powerful predictive models.
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic informatio…
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictor…
New method estimates treatment effects from treated and unlabeled units.
problem Estimating ATEs with missing data and weak supervision.
method Develops semiparametric efficient estimators for ATE in PU learning.
result Constructs estimators that achieve semiparametric efficiency bounds.