CalAPCEipw: Compute APCE using frequentist analysis

In aihuman: Experimental Evaluation of Algorithm-Assisted Human Decision-Making

Compute APCE using frequentist analysis

Description

Estimate propensity score and use Hajek estimator to compute APCE. See S7 for more details.

Usage

CalAPCEipw(data)

Arguments

data	A data.frame or matrix of which columns consists of pre-treatment covariates, a binary treatment (Z), an ordinal decision (D), and an outcome variable (Y). The column names of the latter three should be specified as "Z", "D", and "Y" respectively.

Value

An object of class list with the following elements:

P.D1	An array with dimension (k+1) by (k+2) for quantity P(D(1)=d| R=r), dimension 1 is (k+1) values of D from 0 to k, dimension 2 is (k+2) values of R from 0 to k+1.
P.D0	An array with dimension (k+1) by (k+2) for quantity P(D(0)=d| R=r).
APCE	An array with dimension (k+1) by (k+2) for quantity P(D(1)=d| R=r)-P(D(0)=d| R=r).
P.R	An array with dimension (k+2) for quantity P(R=r) for r from 0 to (k+1).
alpha	An array with estimated alpha.
delta	An array with estimated delta.

Examples

data(synth)
freq_apce <- CalAPCEipw(synth)

aihuman documentation built on April 12, 2025, 1:47 a.m.