Better experimental design by hybridizing binary matching with imbalance optimization
From MaRDI portal
Publication:6059445
DOI10.1002/cjs.11685arXiv2012.03330OpenAlexW4221013622MaRDI QIDQ6059445
Abba M. Krieger, Unnamed Author, Adam Kapelner
Publication date: 2 November 2023
Published in: Canadian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.03330
Cites Work
- Unnamed Item
- Nearly Random Designs with Greatly Improved Balance
- Rerandomization to improve covariate balance in experiments
- Ridge rerandomization: an experimental design strategy in the presence of covariate collinearity
- Matching methods for causal inference: a review and a look forward
- Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique
- Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
- Probability for Statistics and Machine Learning
- Randomization in Clinical Trials
- The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples
- Sequential rerandomization
- OptimalA PrioriBalance in the Design of Controlled Experiments
- Asymptotic theory of rerandomization in treatment–control experiments
- Harmonizing Optimized Designs With Classic Randomization in Experiments
- Rerandomization Strategies for Balancing Covariates Using Pre-Experimental Longitudinal Data
- Matching on‐the‐fly: Sequential allocation with higher power and efficiency
- Rerandomization to Balance Tiers of Covariates
- Bias-Corrected Matching Estimators for Average Treatment Effects
- Forcing a sequential experiment to be balanced
- Optimal multivariate matching before randomization
- Observational studies.
- A matching procedure for sequential experiments that iteratively learns which covariates improve power
This page was built for publication: Better experimental design by hybridizing binary matching with imbalance optimization