Cross-fitted covariance calibration enables chi-squared hypothesis tests for penalized estimating equations that are robust to covariance misspecification under correct conditional mean.
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An empirical Bayes method aggregates estimators from multiple identification functionals for a causal effect, establishing consistency under exact identifiability or growing mean-zero bias regimes while using a working independence device for dependent estimators.
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Hypothesis Testing for Penalized Estimating Equations with Cross-Fitted Covariance Calibration
Cross-fitted covariance calibration enables chi-squared hypothesis tests for penalized estimating equations that are robust to covariance misspecification under correct conditional mean.
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Shrinkage through multiple identifiability
An empirical Bayes method aggregates estimators from multiple identification functionals for a causal effect, establishing consistency under exact identifiability or growing mean-zero bias regimes while using a working independence device for dependent estimators.