Local Balance with Calibration using neural networks creates propensity score weights that enforce local covariate balance and calibration, yielding more stable weights and lower bias in average treatment effect estimates than prior nonparametric approaches.
Estimation of regression coefficients when some regressors are not always observed
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EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.
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Local Balance Calibration for Nonparametric Propensity Score Estimation
Local Balance with Calibration using neural networks creates propensity score weights that enforce local covariate balance and calibration, yielding more stable weights and lower bias in average treatment effect estimates than prior nonparametric approaches.
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A Python Library For Empirical Calibration
EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.