  Due to concerns about parametric model misspecification, there is interest in
using machine learning to adjust for confounding when evaluating the causal
effect of an exposure on an outcome. Unfortunately, exposure effect estimators
that rely on machine learning predictions are generally subject to so-called
plug-in bias, which can render naive p-values and confidence intervals invalid.
Progress has been made via proposals like targeted minimum loss estimation
(TMLE) and more recently double machine learning, which rely on learning the
conditional mean of both the outcome and exposure. Valid inference can then be
obtained so long as both predictions converge (sufficiently fast) to the truth.
Focusing on partially linear regression models, we show that a specific
implementation of the machine learning techniques can yield exposure effect
estimators that have small bias even when one of the first-stage predictions
does not converge to the truth. The resulting tests and confidence intervals
are doubly robust. We also show that the proposed estimators may fail to be
regular when only one nuisance parameter is consistently estimated;
nevertheless, we observe in simulation studies that our proposal can lead to
reduced bias and improved confidence interval coverage in moderate samples.

Methodology