Ebalance: A Stata Package for Entropy Balancing
Jens Hainmueller,Yiqing Xu +1 more
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The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2012) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of specied moment conditions.Abstract:
The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2011) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of speci ed moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.read more
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Attitudes towards vaccines and intention to vaccinate against COVID-19: Implications for public health communications
TL;DR: Amongst vaccine attitudes, intermediate to high levels of mistrust of vaccine benefit and concerns about future unforeseen side effects were the most important determinants of both uncertainty and unwillingness to vaccinate against COVID-19.
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The Effect of Unemployment on the Mental Health of Spouses – Evidence from Plant Closures in Germany
TL;DR: In this article, the effect of an individual's unemployment on the mental health of their spouse was investigated using German Socio-economic Panel Study data and it was shown that unemployment decreases mental health almost as much as for the directly affected individuals.
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Bowling for fascism: social capital and the rise of the Nazi Party
TL;DR: This article examined the role of social capital in the downfall of democracy in interwar Germany and found that dense networks of civic associations such as bowling clubs, choirs, and animal breeders went hand-in-hand with a rapid rise of the Nazi Party.
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Changes in millennial adolescent mental health and health-related behaviours over 10 years: a population cohort comparison study.
TL;DR: Investigation of recent trends in mental ill health and health-related behaviours in two cohorts of UK adolescents in 2005 and 2015 suggests relationships between these factors might be more complex and dynamic in nature than currently understood.
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Preferences for International Redistribution: The Divide Over the Eurozone Bailouts
TL;DR: The authors conducted a systematic analysis of public opinion in the eurozone bailouts using observational and experimental survey data from Germany, the country shouldering the largest share of the EU's financial rescue fund, and found that individuals' own economic standing has limited explanatory power in accounting for their position on bailouts.
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
TL;DR: A unified approach is proposed that makes it possible for researchers to preprocess data with matching and then to apply the best parametric techniques they would have used anyway and this procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.
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Causal Inference without Balance Checking: Coarsened Exact Matching
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Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies
TL;DR: Entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments, obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments.
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Evaluating the Econometric Evaluations of Training Programs with Experimental Data
TL;DR: The National Supported Work Program employed an experimental design that randomly assigned some participants into a treatment group, receiving training, and the rest into a control group receiving no training as mentioned in this paper, and the difference between the post-training earnings of the two groups provided an unbiased estimate of the impact of the program.
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