Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies
From MaRDI portal
Publication:6094081
DOI10.1080/10618600.2022.2116447arXiv2107.00815WikidataQ114638945 ScholiaQ114638945MaRDI QIDQ6094081
Unnamed Author, Kan Chen, Qi Long, Siyu Heng
Publication date: 9 October 2023
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.00815
classificationclusteringstatistical matchingresidual confoundingbiased randomization assumptionimperfect matching
Cites Work
- Unnamed Item
- Matching methods for causal inference: a review and a look forward
- The classification permutation test: a flexible approach to testing for covariate imbalance in observational studies
- Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies
- Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery
- A Conversation with Colin L. Mallows
- Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies
- Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach
- Studentized Sensitivity Analysis for the Sample Average Treatment Effect in Paired Observational Studies
- On Sensitivity Value of Pair-Matched Observational Studies
- Multivariate Matching Methods That Are Monotonic Imbalance Bounding
- Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer
- An Exact Distribution-Free Test Comparing Two Multivariate Distributions based on Adjacency
- Randomization Tests for Weak Null Hypotheses in Randomized Experiments
- Design of observational studies
- Observational studies.
- Bridging preference‐based instrumental variable studies and cluster‐randomized encouragement experiments: Study design, noncompliance, and average cluster effect ratio
- Matching One Sample According to Two Criteria in Observational Studies
This page was built for publication: Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies