ppi_plusplus_mean_est: PPI++ Mean Estimation (Point Estimate)

In ipd: Inference on Predicted Data

PPI++ Mean Estimation (Point Estimate)

Description

Helper function for PPI++ mean estimation (point estimate)

Usage

ppi_plusplus_mean_est(
  Y_l,
  f_l,
  f_u,
  lhat = NULL,
  coord = NULL,
  w_l = NULL,
  w_u = NULL
)

Arguments

Y_l	(vector): n-vector of labeled outcomes.
f_l	(vector): n-vector of predictions in the labeled data.
f_u	(vector): N-vector of predictions in the unlabeled data.
lhat	(float, optional): Power-tuning parameter (see https://arxiv.org/abs/2311.01453). The default value, NULL, will estimate the optimal value from the data. Setting lhat = 1 recovers PPI with no power tuning, and setting lhat = 0 recovers the classical point estimate.
coord	(int, optional): Coordinate for which to optimize lhat = 1. If NULL, it optimizes the total variance over all coordinates. Must be in (1, ..., d) where d is the dimension of the estimand.
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

PPI++: Efficient Prediction Powered Inference (Angelopoulos et al., 2023) https://arxiv.org/abs/2311.01453

Value

float or ndarray: Prediction-powered point estimate of the mean.

Examples

dat <- simdat(model = "mean")

form <- Y - f ~ 1

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)

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

ppi_plusplus_mean_est(Y_l, f_l, f_u)

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