No. 84 Double/debiased Machine Learning for Causal Inference on Survival Function
doubly robust estimatorsurvival analysispseudo-observations
This paper discusses the use of double/debiased machine learning (DML) for estimating the average treatment effect (ATE) on a survival function using pseudo-observations. Through simulations, we demonstrate the double robustness property of our method and its improved performance, compared to existing estimators in the presence of many covariates. In our empirical example, the method is applied in evaluating the effect of the e-learning program participation on the job-finding rate among individuals who are seeking employment.