Ebalance: A Stata Package for Entropy Balancing
By Jens Hainmueller, Yiqing Xu. 2013
859 citations recorded by the source corpus.
The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2012) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of specied moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/54aa7100d7552f7b147e50f35bac8b30af061a12
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In Statistics and Probability
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Citations in the corpus
- Preferences for International Redistribution: The Divide over the Eurozone Bailouts: PREFERENCES FOR INTERNATIONAL REDISTRIBUTION (2014, positive): “To address these imbalances, we use entropy balancing (Hainmueller 2012; Hainmueller and Xu 2013) to reweight the data from the online and phone survey to match the demographic margins from the voter population.”
- Attitudes towards vaccines and intention to vaccinate against COVID-19: Implications for public health communications (2020, positive): “A multivariate reweighting method was implemented using the Stata user written command ‘ebalance’ [24].”
- Switching to self-employment can be good for your health (2019, methodology): “18 I used the Stata user-written command –ebalance- for the entropy balancing (Hainmueller and Xu, 2013).”
- Entropy-balanced accruals (2020, methodology): “On this latter point, EB is easy to implement in Stata (Hainmueller and Xu, 2013) and requires relatively few discretionary choices by the researcher, both features which should facilitate (when necessary) the replication of prior results.3 Given EB’s benefits and ease of implementation, we…”