  Unlike parametric regression, machine learning (ML) methods do not generally
require precise knowledge of the true data generating mechanisms. As such,
numerous authors have advocated for ML methods to estimate causal effects.
Unfortunately, ML algorithms can perform worse than parametric regression. We
demonstrate the performance of ML-based single- and double-robust estimators.
We use 100 Monte Carlo samples with sample sizes of 200, 1200, and 5000 to
investigate bias and confidence interval coverage under several scenarios. In a
simple confounding scenario, confounders were related to the treatment and the
outcome via parametric models. In a complex confounding scenario, the simple
confounders were transformed to induce complicated nonlinear relationships. In
the simple scenario, when ML algorithms were used, double-robust estimators
were superior to single-robust estimators. In the complex scenario,
single-robust estimators with ML algorithms were at least as biased as
estimators using misspecified parametric models. Double-robust estimators were
less biased, but coverage was well below nominal. The use of sample splitting,
inclusion of confounder interactions, reliance on a richly specified ML
algorithm, and use of doubly robust estimators was the only explored approach
that yielded negligible bias and nominal coverage. Our results suggest that ML
based singly robust methods should be avoided.
