ppi_ols: PPI OLS

In ipd: Inference on Predicted Data

PPI OLS

Description

Helper function for prediction-powered inference for OLS estimation

Usage

ppi_ols(X_l, Y_l, f_l, X_u, f_u, w_l = NULL, w_u = NULL)

Arguments

X_l	(matrix): n x p matrix of covariates in the labeled data.
Y_l	(vector): n-vector of labeled outcomes.
f_l	(vector): n-vector of predictions in the labeled data.
X_u	(matrix): N x p matrix of covariates in the unlabeled data.
f_u	(vector): N-vector of predictions in the unlabeled data.
w_l	(ndarray, optional): Sample weights for the labeled data set. Defaults to a vector of ones.
w_u	(ndarray, optional): Sample weights for the unlabeled data set. Defaults to a vector of ones.

Details

Prediction Powered Inference (Angelopoulos et al., 2023) https://www.science.org/doi/10.1126/science.adi6000

Value

(list): A list containing the following:

est

(vector): vector of PPI OLS regression coefficient estimates.

se

(vector): vector of standard errors of the coefficients.

rectifier_est

(vector): vector of the rectifier OLS regression coefficient estimates.

Examples

dat <- simdat()

form <- Y - f ~ X1

X_l <- model.matrix(form, data = dat[dat$set_label == "labeled",])

Y_l <- dat[dat$set_label == "labeled", all.vars(form)[1]] |> matrix(ncol = 1)

f_l <- dat[dat$set_label == "labeled", all.vars(form)[2]] |> matrix(ncol = 1)

X_u <- model.matrix(form, data = dat[dat$set_label == "unlabeled",])

f_u <- dat[dat$set_label == "unlabeled", all.vars(form)[2]] |> matrix(ncol = 1)

ppi_ols(X_l, Y_l, f_l, X_u, f_u)

ipd documentation built on April 4, 2025, 4:41 a.m.