To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates

DOI10.1080/01621459.2022.2123814arXiv2108.00152OpenAlexW3192456749MaRDI QIDQ6154005

Peng Ding, Anqi Zhao

Publication date: 19 March 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.00152

zbMATH Keywords

efficiencyimputationrobust standard errorrandomized controlled trialregression adjustmentmissingness pattern

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Treatment effects estimation with missing not at random data without outcome modeling

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
• Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique
• Covariate-adjusted Fisher randomization tests for the average treatment effect
• On regression adjustments to experimental data
• Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression
• Inference and missing data
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Moments and Distributions of Estimates of Population Parameters from Fragmentary Samples
• Statistical Analysis with Missing Data, Third Edition
• Pattern-Mixture Models for Multivariate Incomplete Data
• Leveraging random assignment to impute missing covariates in causal studies
• Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments
• Rerandomization and Regression Adjustment
• Causal inference with confounders missing not at random
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Missing-Data Methods for Generalized Linear Models
• Revisiting regression adjustment in experiments with heterogeneous treatment effects
• Design of observational studies

This page was built for publication: To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates