estimate_nu: Estimate nu

In PLUCR: Policy Learning Under Constraint

Estimate nu

Description

This function trains conditional mean of adverse event outcome models for treated and control groups using SuperLearner, applying cross-validation to compute out-of-fold estimates.

Usage

estimate_nu(
  Xi,
  A,
  X,
  folds,
  SL.library = c("SL.glm", "SL.mean"),
  V = 2L,
  threshold = 0.01
)

Arguments

Xi	A numeric vector or matrix of adverse events outcomes.
A	A binary vector or matrix of length n indicating treatment assignment (0 or 1).
X	A matrix or data frame of covariates of size n x d (input data in ⁠[0,1]⁠).
folds	A list of cross-validation folds (e.g., a list of indices for each fold).
SL.library	Vector of libraries for training SuperLearner (c("SL.glm", "SL.mean") by default).
V	Number of folds inside the SuperLearner (2L by default).
threshold	A numeric scalar that sets the minimum allowed value for upper and lower bound estimations (1e-2 by default). Constrains estimation to ⁠[threshold, 1 - threshold]⁠.

Value

A fold-specific function predicting adverse event outcome (Xi) given treatment (A) and covariates (X)

PLUCR documentation built on March 30, 2026, 5:08 p.m.