Background: 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. Methods: We conducted a simulation study to assess the
performance of several different estimators for the average causal effect. The data
generating mechanisms for the simulated treatment and outcome included log-transforms …