Sensitivity analysis of individual treatment effects with PAC-type guarantees

cfsens_pac(
  X,
  Y,
  T,
  alpha,
  delta,
  null_type = c("sharp", "negative", "positive"),
  score_type = c("cqr"),
  ps_fun = regression_forest,
  ps = NULL,
  pred_fun = quantile_forest,
  train_prop = 0.75,
  train_id = NULL
)

Arguments

X	covariates.
Y	the observed outcome vector.
T	the vector of treatment assignments.
alpha	the target confidence level.
delta	the confidence level over the randomness over the calibration set.
null_type	the null to be tested that takes value in {"sharp", "negative", "positive"}. See Details.
score_type	the type of nonconformity scores. The default is "cqr".
ps_fun	a function that models the treatment assignment mechanism. The default is "regression_forest".
ps	a vector of propensity score. The default is NULL.
pred_fun	a function that models the potential outcome conditional on the covariates. The default is "quantile_forest".
train_prop	proportion of units used for training. The default is 75\ train_idThe index of the units used for training. The default is NULL.