Entropy Balancing for Continuous Treatments

Stefan Tübbicke

No 21, CEPA Discussion Papers from Center for Economic Policy Analysis

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) by extending the original entropy balancing methodology of Hainmüller (2012). In order to estimate balancing weights, the proposed approach solves a globally convex constrained optimization problem, allowing for much more computationally efficient implementation compared to other available methods. EBCT weights reliably eradicate Pearson correlations between covariates and the continuous treatment variable. This is the case even when other methods based on the generalized propensity score tend to yield insufficient balance due to strong selection into different treatment intensities. Moreover, the optimization procedure is more successful in avoiding extreme weights attached to a single unit. Extensive Monte-Carlo simulations show that treatment effect estimates using EBCT display similar or lower bias and uniformly lower root mean squared error. 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: 2020-10
New Economics Papers: this item is included in nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Entropy Balancing for Continuous Treatments (2022)
Working Paper: Entropy Balancing for Continuous Treatments (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pot:cepadp:21

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