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Entropy balancing as an estimation command

Author

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  • Ben Jann
Abstract
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.

Suggested Citation

  • Ben Jann, 2021. "Entropy balancing as an estimation command," University of Bern Social Sciences Working Papers 39, University of Bern, Department of Social Sciences, revised 16 Aug 2021.
    • Handle: RePEc:bss:wpaper:39
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    File URL: https://boris.unibe.ch/157883/15/jann-2021-ebalfit.pdf
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    References listed on IDEAS

    as
    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    3. Keith Kranker & Laura Blue & Lauren Vollmer Forrow, 2021. "Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights," The American Statistician, Taylor & Francis Journals, vol. 75(3), pages 276-287, July.
    4. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    5. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    6. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
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    Cited by:

    1. Ben Jann, 2021. "dstat: A new command for the analysis of distributions," 2021 Stata Conference 1, Stata Users Group.
    2. Aymo Brunetti & Konstantin B chel & Martina Jakob & Ben Jann & Daniel Steffen, 2021. "Inadequate Teacher Content Knowledge and What to Do About It: Evidence from El Salvador," Diskussionsschriften dp2114, Universitaet Bern, Departement Volkswirtschaft.

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    More about this item

    Keywords

    entropy balancing; reweighting; inverse probability weighting; IPW; influence function;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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