Using Approximation Algorithms to Build Evidence Factors and Related Designs for Observational Studies

DOI10.1080/10618600.2019.1584900OpenAlexW2924899695WikidataQ128192821 ScholiaQ128192821MaRDI QIDQ139209

Paul R. Rosenbaum, Bikram Karmakar, Dylan S. Small, Dylan S. Small, Bikram Karmakar, Paul R. Rosenbaum

Publication date: 13 May 2019

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2019.1584900

zbMATH Keywords

matchingapproximation algorithmcausal inferencecontrol groupsevidence factorsmultiple observational study

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Selecting intervals to optimize the design of observational studies subject to fine balance constraints, Rejoinder: Matching methods for observational studies derived from large administrative databases, approxmatch, Polymatching algorithm in observational studies with multiple treatment groups

Uses Software

• R
• ggplot2
• mipmatch
• nbpMatching
• sensitivitymv
• finebalance
• rcbalance
• sensitivitymw
• senstrat
• evidenceFactors
• DOS

Cites Work

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