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.
Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies
4 Pith papers cite this work. Polarity classification is still indexing.
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Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.
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.
citing papers explorer
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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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Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
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Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.
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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.