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An empirical exploration of entropy balancing in estimating treatmenteffects: Insights from simulation and two applied biomedical studies |
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| รหัสดีโอไอ | |
| Creator | 1. Lateef Amusa 2. Temesgen Zewotir 3. Delia North |
| Title | An empirical exploration of entropy balancing in estimating treatmenteffects: Insights from simulation and two applied biomedical studies |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2021 |
| Journal Title | Songklanakarin Journal of Science and Technology (SJST) |
| Journal Vol. | 43 |
| Journal No. | 2 |
| Page no. | 406-413 |
| Keyword | entropy balancing, Monte Carlo simulation, observational studies, propensity score weighting, treatment effect |
| URL Website | https://rdo.psu.ac.th/sjstweb/index.php |
| ISSN | 0125-3395 |
| Abstract | We present entropy balancing a relatively new technique for estimating treatment effects, which has been under-utilisedin the applied biomedical literature. Our objective is to share our experiences learned from using entropy balancing in nonexperimental studies, via Monte Carlo simulations and two empirical examples. We used the inverse probability of treatmentweighting method for benchmarking the performance of entropy balancing. Entropy balancing had remarkably superiorperformance in terms of covariate balance and efficient estimation of treatment effects. Entropy balancing does not requireextensive tweaking of the propensity score specification to achieve the optimal covariate balance. Instead, it directly incorporatescovariate balance into the weight function that is applied to the sample units. Notably, the excellent performance of entropybalancing is not only on the first moment (mean) but also on higher-order moments. However, we also highlighted the situationswhere entropy balancing may fail or not have optimal performance. Entropy balancing merits more widespread adoption inbiomedical research. |
