Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies

DOI10.1287/ijoc.2013.0583zbMath1304.62139OpenAlexW2167961909WikidataQ130493396 ScholiaQ130493396MaRDI QIDQ2940541

Jacobson, Sheldon H., Jason J. Sauppe, Edward C. Sewell

Publication date: 27 January 2015

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/ijoc.2013.0583

zbMATH Keywords

computational complexitymatchingmixed integer programmingobservational studiesapproximation algorithmscausal inferencefine balancecomparative effectiveness researchbalance optimization

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Design of statistical experiments (62K99) Sampling theory, sample surveys (62D05) Applications of mathematical programming (90C90) Medical applications (general) (92C50)

Related Items (6)

A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference ⋮ Building Representative Matched Samples With Multi-Valued Treatments in Large Observational Studies ⋮ Estimating causal effects with optimization-based methods: a review and empirical comparison ⋮ Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients ⋮ Selecting intervals to optimize the design of observational studies subject to fine balance constraints ⋮ Network flow methods for the minimum covariate imbalance problem

Uses Software

• mipmatch
• nbpMatching
• StatMatch
• finebalance

Cites Work

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• Covariate balance in simple, stratified and clustered comparative studies
• Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes
• A threshold of ln n for approximating set cover
• The central role of the propensity score in observational studies for causal effects
• Bias Reduction Using Mahalanobis-Metric Matching
• An analysis of approximations for maximizing submodular set functions—I
• Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery
• Multivariate Matching Methods That Are Monotonic Imbalance Bounding
• Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data
• Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer
• Maximum matching and a polyhedron with 0,1-vertices

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