  Modern causal inference methods allow machine learning to be used to weaken
parametric modeling assumptions. However, the use of machine learning may
result in complications for inference. Doubly-robust cross-fit estimators have
been proposed to yield better statistical properties.
  We conducted a simulation study to assess the performance of several
different estimators for the average causal effect (ACE). The data generating
mechanisms for the simulated treatment and outcome included log-transforms,
polynomial terms, and discontinuities. We compared singly-robust estimators
(g-computation, inverse probability weighting) and doubly-robust estimators
(augmented inverse probability weighting, targeted maximum likelihood
estimation). Nuisance functions were estimated with parametric models and
ensemble machine learning, separately. We further assessed doubly-robust
cross-fit estimators.
  With correctly specified parametric models, all of the estimators were
unbiased and confidence intervals achieved nominal coverage. When used with
machine learning, the doubly-robust cross-fit estimators substantially
outperformed all of the other estimators in terms of bias, variance, and
confidence interval coverage.
  Due to the difficulty of properly specifying parametric models in high
dimensional data, doubly-robust estimators with ensemble learning and
cross-fitting may be the preferred approach for estimation of the ACE in most
epidemiologic studies. However, these approaches may require larger sample
sizes to avoid finite-sample issues.
