Cover Image

Outcome-Assisted Multiple Imputation of Missing Treatments

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feldman, Joseph, Reiter, Jerome P.
Format:	Preprint
Published:	2025
Subjects:	Methodology Applications Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Imputation of Nonignorable Missing Data in Surveys Using Auxiliary Margins Via Hot Deck and Sequential Imputation
by: Yang, Yanjiao, et al.
Published: (2024)

Gaussian Copula Models for Nonignorable Missing Data Using Auxiliary Marginal Quantiles
by: Feldman, Joseph, et al.
Published: (2024)

Evaluating Binary Outcome Classifiers Estimated from Survey Data
by: Wadekar, Adway S., et al.
Published: (2023)

Constructing a T-test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data
by: Lu, Minxin, et al.
Published: (2023)

Variational Bayesian Multiple Imputation in High-Dimensional Regression Models With Missing Responses
by: Li, Qiushuang, et al.
Published: (2025)

Multiple Imputation Method for High-Dimensional Neuroimaging Data
by: Lu, Tong, et al.
Published: (2023)

A Sensitivity Analysis Framework for Quantifying Confidence in Decisions in the Presence of Data Uncertainty
by: Wadekar, Adway S., et al.
Published: (2025)

Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework
by: Binette, Olivier, et al.
Published: (2024)

Index Date Imputation For Survival Outcomes for Externally Controlled Trials
by: Coent, Q. Le, et al.
Published: (2025)

The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
by: McGowan, Lucy D'Agostino
Published: (2025)

Causal Inference with Outcomes Truncated by Death and Missing Not at Random
by: Li, Wei, et al.
Published: (2024)

Multiple Imputation of Hierarchical Nonlinear Time Series Data with an Application to School Enrollment Data
by: Liu, Daphne H., et al.
Published: (2024)

Difference-in-differences Design with Outcomes Missing Not at Random
by: Shin, Sooahn
Published: (2024)

Bayesian Quantile Regression with Subset Selection: A Decision Analysis Perspective
by: Feldman, Joseph, et al.
Published: (2023)

Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests
by: Samii, Cyrus, et al.
Published: (2023)

Robust Emax Model Fitting: Addressing Nonignorable Missing Binary Outcome in Dose-Response Analysis
by: Zhang, Jiangshan, et al.
Published: (2024)

Sequential Hierarchical Regression Imputation with Variable Selection Routines
by: Li, Qiushuang, et al.
Published: (2025)

Augmented Doubly Robust Post-Imputation Inference for Proteomic Data
by: Moon, Haeun, et al.
Published: (2024)

A Comparative Study of Imputation Methods for Multivariate Ordinal Data
by: Wongkamthong, Chayut, et al.
Published: (2020)

Multiple Imputation for Nonresponse in Complex Surveys Using Design Weights and Auxiliary Margins
by: Xu, Kewei, et al.
Published: (2024)

Introducing Powerwise (PWR): A pairwise and Power Rating method for selecting at-large teams to the NCAA Division I Men's Lacrosse Championship
by: Feldman, Lawrence, et al.
Published: (2025)

Treatment heterogeneity with right-censored outcomes using grf
by: Sverdrup, Erik, et al.
Published: (2023)

Treatment Effect Heterogeneity and Importance Measures for Multivariate Continuous Treatments
by: Shin, Heejun, et al.
Published: (2024)

Establishing the Parallels and Differences Between Right-Censored and Missing Covariates
by: Vazquez, Jesus E., et al.
Published: (2024)

Exact and Conservative Inference for the Average Treatment Effect in Stratified Experiments with Binary Outcomes
by: Li, Jiaxun, et al.
Published: (2025)

Estimating Interpretable Heterogeneous Treatment Effect with Causal Subgroup Discovery in Survival Outcomes
by: Bo, Na, et al.
Published: (2024)

Correcting for Missing Data When Evaluating Surrogate Markers in a Clinical Trial
by: Lotspeich, Sarah C., et al.
Published: (2026)

Doubly Robust Targeted Estimation of Conditional Average Treatment Effects for Time-to-event Outcomes with Competing Risks
by: Li, Runjia, et al.
Published: (2024)

Model Selection for Causal Modeling in Missing Exposure Problems
by: Shi, Yuliang, et al.
Published: (2024)

Analysis of Linked Files: A Missing Data Perspective
by: Kamat, Gauri, et al.
Published: (2024)

A nonstationary spatial model of PM2.5 with localized transfer learning from numerical model output
by: Gong, Wenlong, et al.
Published: (2025)

Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses
by: Mai, The Tien
Published: (2025)

Personalized Imputation in metric spaces via conformal prediction: Applications in Predicting Diabetes Development with Continuous Glucose Monitoring Information
by: Matabuena, Marcos, et al.
Published: (2024)

Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing the Normal-with-Optional-Shrinkage Data Model Class
by: Alkema, Leontine, et al.
Published: (2024)

Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
by: Cattaneo, Matias D., et al.
Published: (2022)

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
by: Du, Jin-Hong, et al.
Published: (2024)

Adjusting for Selection Bias Due to Missing Eligibility Criteria in Emulated Target Trials
by: Benz, Luke, et al.
Published: (2024)

Fusing Individualized Treatment Rules Using Secondary Outcomes
by: Gao, Daiqi, et al.
Published: (2024)

Bayesian Bootstrap based Gaussian Copula Model for Mixed Data with High Missing Rates
by: Kim, Seongmin, et al.
Published: (2025)

Inverse Probability Weighting of Count Exposures in the Presence of Missing Data: A Simulation Study
by: Danka, Martin N., et al.
Published: (2026)