gen_data_mapl: Example data generating process from Offline Multi-Action...
In policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees
gen_data_mapl R Documentation
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 June 22, 2024, 9:47 a.m.
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| gen_data_mapl | R Documentation |
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 |
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).
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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