Robust conformal counterfactual inference with PAC-type guarantee

cfsens_cf_pac(
  X,
  Y,
  T,
  Gamma,
  alpha,
  delta,
  side = c("two", "above", "below"),
  score_type = c("cqr"),
  ps_fun = regression_forest,
  ps = NULL,
  pred_fun = quantile_forest,
  train_pop = 0.75,
  train_id = NULL
)

Arguments

X	covariates.
Y	the observed outcome vector.
T	the vector of treatment assignments.
Gamma	The confounding level.
alpha	the target confidence level.
delta	the target confidence level over the randomness of calibration set.
side	the type of predictive intervals that takes value in {"two", "above", "below"}. 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_id	The index of the units used for training. The default is NULL.
train_prop	proportion of units used for training. The default is 75\