Entropy balancing as an estimation command

Jann, Ben (3 August 2021). Entropy balancing as an estimation command (University of Bern Social Sciences Working Papers 39). Bern: University of Bern, Department of Social Sciences

Preview	Text (revised version: August 16, 2021) jann-2021-ebalfit.pdf - Published Version Available under License BORIS Standard License. Download (882kB) | Preview
Preview	Text (revised version: August 5, 2021) jann-2021-ebalfit.pdf - Published Version Available under License BORIS Standard License. Download (860kB) | Preview
Preview	Text (original version: August 3, 2021) jann-2021-ebalfit.pdf - Published Version Available under License BORIS Standard License. Download (832kB) | Preview

Entropy balancing is a popular reweighting technique that provides an alternative to approaches such as, for example, inverse probability weighting (IPW) based on a logit or probit model. Even if the balancing weights resulting from the procedure will be of primary interest in most applications, it is noteworthy that entropy balancing can be represented as a simple regression-like model. An advantage of treating entropy balancing as a parametric model is that it clarifies how the reweighting affects statistical inference. In this article I present a new Stata command called -ebalfit- that estimates such a model including the variance-covariance matrix of the estimated coefficients. The balancing weights are then obtained as model predictions. Variance estimation is based on influence functions, which can be stored for further use, for example, to obtain consistent standard errors for statistics computed from the reweighted data.

Interest & Impact

Downloads

2519 since deposited on 06 Aug 2021
1308 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item