estimate_mu: Estimate mu

In PLUCR: Policy Learning Under Constraint

Estimate mu

Description

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

Usage

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

Arguments

Y	A numeric vector or matrix of length n representing primary outcomes (in ⁠[0,1]⁠).
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 primary outcome (Y) given treatment (A) and covariates (X)

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