lp_r_learner: Lp-R-Learner

In matteobonvini/drl.cate: What the Package Does (One Line, Title Case)

Lp-R-Learner

Description

This function computes the Lp-R-Learner approach to CATE estimation.

Usage

lp_r_learner(x0, y, a, x, mu.x, pi.x, basis, order_basis, kernel)

Arguments

x0	evaluation points, i.e. E(Y^1 - Y^0 | x0)
y	vector of outcomes
a	vector of treatments
x	matrix of covariates
mu.x	a function with arguments y, x, new.x computing the regression of y on x and evaluating it at new.x.
pi.x	a function with arguments a, x, new.x computing the propensity score and evaluating it at new.x.
basis	a function with arguments x and j returning the j^th basis element applied to x, e.g. x^j. It will be the building block to compute a tensor product basis.
order_basis	the order of the basis
kernel	a function with arguments x and x_0 returning K((x - x0) / h) / h

Value

a list containing the following components:

est	estimate of the CATE at x0
fold_k_est	length(x0)xnsplits matrix with estimates of the CATE at x0 in each fold.

matteobonvini/drl.cate documentation built on Nov. 10, 2024, 12:20 a.m.