Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

By Jens Hainmueller. Political Analysis, 2012

871 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fbb13f64bfffc7a12d4a7e17c7c140eb1466f93f

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Citations in the corpus

Entropy Balancing for Continuous Treatments (2020, positive): “First, it extends the entropy balancing approach by Hainmueller (2012) for the estimation of balancing weights fromthebinary treatment framework to thecontextof continuous treatments.”
Entropy Balancing is Doubly Robust (2015, positive): “In this paper, we study a method of this kind called Entropy Balancing (hereafter EB) that is proposed by Hainmueller (2011).”
Ebalance: A Stata Package for Entropy Balancing (2013, neutral): “As described in Hainmueller (2012) the user has to be careful not to impose unrealistic or even inconsistent balance constraints.”
Misinformation and Motivated ReasoningResponses to Economic News in a Politicized Environment (2016, neutral): “Entropy balancing is a technique that re-weights the sample to ensure that the control group’s characteristics are equivalent to the treatment group on the specified co-variates (Hainmueller, 2011).”
Social Media, News Consumption, and Polarization: Evidence from a Field Experiment (2020, positive): “In this section, I reweight the sample to match the national population using the entropy weighting procedure (Hainmueller, 2012).”
Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies (2006, neutral): “…has the benefit of being doubly robust (Robins, Rotnitzky, & Zhao, 1994, 1995), and a proposal to adapt maximum entropy weighting, which has long been used in the survey data literature to match moments using auxiliary information, to the case of estimating treatment effects (Hainmueller, 2012).”