gen_data_mapl: Example data generating process from Offline Multi-Action...

In policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization

Description

The DGP from section 6.4.1 in Zhou, Athey, and Wager (2023): There are d=3 actions (a_0,a_1,a_2) which depend on 3 regions the covariates X \sim U[0,1]^p reside in. Observed outcomes: Y \sim N(\mu_{a_i}(X_i), 4)

Usage

gen_data_mapl(n, p = 10, sigma2 = 4)

Arguments

n	Number of observations X.
p	Number of features (minimum 7). Default is 10.
sigma2	Noise variance. Default is 4.

Value

A list with realized action a_i, region r_i, conditional mean \mu, outcome Y and covariates X

References

Zhou, Zhengyuan, Susan Athey, and Stefan Wager. "Offline multi-action policy learning: Generalization and optimization." Operations Research 71.1 (2023).

policytree documentation built on July 9, 2023, 6:30 p.m.