Entropy balancing: a maximum-entropy reweighting scheme to adjust for coverage error

Entropy balancing: a maximum-entropy reweighting scheme to adjust for coverage error This paper presents a newly available technique to adjust for bias in non-probabilistically selected samples. To date, applications of this innovative technique—termed entropy balancing—have been restricted to evaluation settings, where the goal is to reduce model dependence prior to the estimation of treatment effects. In a novel application, we demonstrate the technique’s utility in cases where the goal is to correct for sample bias originating in coverage error. The appeal of entropy balancing in this latter setting lies in its capacity to optimise the twin goals of improved balance in covariate distribution and maximum retention of information. Entropy balancing combines the opportunity to incorporate a large set of moment conditions in the calculation of weights, with the ability to directly implement exact balance. The technique thus builds upon the theoretical appeal of the more widely known and applied propensity score adjustment method, while addressing that method’s practical limitations. We demonstrate the utility of the entropy balancing technique empirically, through an example using the Young Lives Project survey data for rural Andhra Pradesh, South India. We conclude by summarising the potential of this procedure to contribute to robust survey-based research more widely. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Entropy balancing: a maximum-entropy reweighting scheme to adjust for coverage error

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Publisher
Springer Netherlands
Copyright
Copyright © 2015 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-015-0235-8
Publisher site
See Article on Publisher Site

Abstract

This paper presents a newly available technique to adjust for bias in non-probabilistically selected samples. To date, applications of this innovative technique—termed entropy balancing—have been restricted to evaluation settings, where the goal is to reduce model dependence prior to the estimation of treatment effects. In a novel application, we demonstrate the technique’s utility in cases where the goal is to correct for sample bias originating in coverage error. The appeal of entropy balancing in this latter setting lies in its capacity to optimise the twin goals of improved balance in covariate distribution and maximum retention of information. Entropy balancing combines the opportunity to incorporate a large set of moment conditions in the calculation of weights, with the ability to directly implement exact balance. The technique thus builds upon the theoretical appeal of the more widely known and applied propensity score adjustment method, while addressing that method’s practical limitations. We demonstrate the utility of the entropy balancing technique empirically, through an example using the Young Lives Project survey data for rural Andhra Pradesh, South India. We conclude by summarising the potential of this procedure to contribute to robust survey-based research more widely.

Journal

Quality & QuantitySpringer Journals

Published: Jun 20, 2015

References

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