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.
Parametrization, Prior Independence, and the Semiparametric Bernstein-von Mises Theorem for the Partially Linear Model
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abstract
I prove a semiparametric Bernstein-von Mises theorem for a partially linear regression model with independent priors for the low-dimensional parameter of interest and the infinite-dimensional nuisance parameters. My result avoids a challenging prior invariance condition that arises from a loss of information associated with not knowing the nuisance parameter. The key idea is to employ a feasible reparametrization of the partially linear regression model that reflects the semiparametric structure of the model. This allows a researcher to assume independent priors for the model parameters while automatically accounting for the loss of information associated with not knowing the nuisance parameters. The theorem is verified for uniform wavelet series priors and Mat\'{e}rn Gaussian process priors.
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econ.EM 1years
2025 1verdicts
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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.