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Entropy Balancing for Continuous Treatments

Stefan Tübbicke

Journal of Econometric Methods, 2022, vol. 11, issue 1, 71-89

Abstract: Interest in evaluating the effects of continuous treatments has been on the rise recently. To facilitate the estimation of causal effects in this setting, the present paper introduces entropy balancing for continuous treatments (EBCT) – an intuitive and user-friendly automated covariate balancing scheme – by extending the original entropy balancing methodology of Hainmueller, J. 2012. “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20 (1): 25–46. In order to estimate balancing weights, the proposed approach solves a globally convex constrained optimization problem, allowing for computationally efficient software implementation. EBCT weights reliably eradicate Pearson correlations between covariates (and their transformations) and the continuous treatment variable. As uncorrelatedness may not be sufficient to guarantee consistent estimates of dose–response functions, EBCT also allows to render higher moments of the treatment variable uncorrelated with covariates to mitigate this issue. Empirical Monte-Carlo simulations suggest that treatment effect estimates using EBCT display favorable properties in terms of bias and root mean squared error, especially when balance on higher moments of the treatment variable is sought. These properties make EBCT an attractive method for the evaluation of continuous treatments. Software implementation is available for Stata and R.

Keywords: balancing weights; continuous treatment; Monte–Carlo simulation; observational studies (search for similar items in EconPapers)
JEL-codes: C14 C21 C87 (search for similar items in EconPapers)
Date: 2022
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Working Paper: Entropy Balancing for Continuous Treatments (2020) Downloads
Working Paper: Entropy Balancing for Continuous Treatments (2020) Downloads
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DOI: 10.1515/jem-2021-0002

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