  Observational cohort studies are increasingly being used for comparative
effectiveness research to assess the safety of therapeutics. Recently, various
doubly robust methods have been proposed for average treatment effect
estimation by combining the treatment model and the outcome model via different
vehicles, such as matching, weighting, and regression. The key advantage of
doubly robust estimators is that they require either the treatment model or the
outcome model to be correctly specified to obtain a consistent estimator of
average treatment effects, and therefore lead to a more accurate and often more
precise inference. However, little work has been done to understand how doubly
robust estimators differ due to their unique strategies of using the treatment
and outcome models and how machine learning techniques can be combined to boost
their performance. Here we examine multiple popular doubly robust methods and
compare their performance using different treatment and outcome modeling via
extensive simulations and a real-world application. We found that incorporating
machine learning with doubly robust estimators such as the targeted maximum
likelihood estimator gives the best overall performance. Practical guidance on
how to apply doubly robust estimators is provided.
