The authors extend the DR-learner and EP-learner to handle outcomes missing at random by adding inverse-probability-of-censoring weights, and show the resulting estimators are oracle efficient.
Metalearners for estimating heterogeneous treatment effects using machine learning
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Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
The authors extend the DR-learner and EP-learner to handle outcomes missing at random by adding inverse-probability-of-censoring weights, and show the resulting estimators are oracle efficient.