Machine learning for causal inference: on the use of cross-fit estimators

Understand

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).