On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

Lin Liu, Rajarshi Mukherjee, James M. Robins

For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators ${\hat{\psi }}_1$ are the state-of-the-art, incorporating the good prediction performance…