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Examination of Entropy balancing technique for estimating some standard measures of treatment effects: A simulation study


 
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1. Title Title of document Examination of Entropy balancing technique for estimating some standard measures of treatment effects: A simulation study
 
2. Creator Author's name, affiliation, country Lateef Amusa; University of Kwazulu-Natal; South Africa
 
2. Creator Author's name, affiliation, country Temesgen Zewotir; South Africa
 
2. Creator Author's name, affiliation, country Delia North; Nigeria
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Entropy balancing; Monte Carlo simulation; Observational studies; Propensity score weighting; Treatment effect; odds ratios; hazard ratios; rate ratios
 
4. Description Abstract

In observational studies, propensity score weighting methods are regarded as the conventional standard for estimating the effects of treatments on outcomes. We introduce entropy balancing, which despite its excellent conceptual properties, has been under-utilized in the applied studies. Using an extensive series of Monte Carlo simulations, we evaluated the performance of entropy balancing, in estimating difference in means, marginal odds ratios, rate ratios and hazard ratios. The performance of entropy balancing was relatively compared with that of inverse probability of treatment weighting using the propensity score. We found that entropy balancing outperformed the IPW method in estimating difference in means, marginal odds ratios, and hazard ratios, but when estimating marginal rate ratios, IPW performed better. Entropy balancing produced more biased estimates in many cases. However, the entropy balancing algorithm is capable of controlling bias by loosening the tightening of the pre-specified tolerance on covariate balance. We report findings as to when one technique is better than the other with no proclamation on whether one method is in every case superior to the other. Entropy balancing merits more widespread adoption in applied studies.
 
5. Publisher Organizing agency, location Coordinamento SIBA - Università del Salento
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2019-10-14
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format pdf
 
10. Identifier Uniform Resource Identifier http://siba-ese.unisalento.it/index.php/ejasa/article/view/20409
 
10. Identifier Digital Object Identifier 10.1285/i20705948v12n2p491
 
11. Source Publication/conference title; vol., no. (year) Electronic Journal of Applied Statistical Analysis; Vol 12, No 2 (2019): Electronic Journal of Applied Statistical Analysis
 
12. Language English=en en
 
13. Relation Supp. Files
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
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