A Bayesian re-parameterization of the partially linear model recovers the causal effect from the reduced-form error covariance, avoiding regularization-induced confounding and matching double machine learning asymptotics.
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Bayesian Double Machine Learning for Causal Inference
A Bayesian re-parameterization of the partially linear model recovers the causal effect from the reduced-form error covariance, avoiding regularization-induced confounding and matching double machine learning asymptotics.